Uncertainty in position estimation of autonomous transport systems

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

This report consists of 79 pages and 0 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning

Specialization: Transport Engineering and Logistics

Report number: 2012.TEL.7738

Title:

Uncertainty in position estimation

of autonomous transport systems

Author:

B.B. de Keyzer

Title (in Dutch) Onzekerheid in de schatting van de posititie van autonome transportsystemen

Assignment: literature

Confidential: no

Initiator (university): prof.dr.ir. G. Lodewijks Initiator (company): -

Supervisor: dr. R. Negenborn

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Uncertainty in position estimation of autonomous

transport systems

Bj¨orn de Keyzer

Transport Engineering and Logistics,

TU Delft

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Abstract

A problem in autonomous transport systems is the uncertainty of measurements. Espe-cially uncertainty in position estimation needs to be minimized in order for a transport system to function fully autonomous. If a transport system does not know where it is, it cannot determine its next action to get closer to its goal. In this report an overview is made of autonomous transport systems for internal transport in companies. Transport systems are discussed both for indoor and outdoor use, for the transport of parcels, containers and general cargo. The sources of noise, uncertainty and disturbances are identified, both for indoor and outdoor systems. An overview of sensors used for po-sition estimation is made and the distinction is made between relative and absolute position measurements. The link between these sensors and the sources of noise, un-certainty and disturbances is made. Each sensor has its own unun-certainty, it is shown that certain combinations of sensors can decrease the uncertainty of the system as a whole. Therefore methods for combining information from multiple sensors in transport sys-tems are discussed, called fusion methods. Kalman Filters, and its varieties used in transport applications as well as other fusion methods are discussed. An overview is made of literature with the fusion methods and their applications in transport systems.

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Introduction

This report describes the uncertainty in position estimation in autonomous transport systems, and comes with solutions to decrease that uncertainty. In present times, more and more processes become automated, to increase the efficiency or production of the process, or to decrease the cost. In the field of transport, research into automation has been done for decades and is still going [28]. Transport is everywhere, it exists in many varieties. There is transport between one company and another, between two cities, maybe between two countries, or even between continents. This transport can be achieved by road, rail, water, air or pipeline. This is the type of transport people think of when the subject is mentioned, because that is visible for them. Less visible, but not less important, is transport within companies, either indoor or outdoor. This transport can also be done by road, rail, water or pipeline, but the systems used for in-ternal transport are often different than the systems for transport outside the companies. Transport routes within companies are often from a storage facility to a production area and vice versa. Therefore, specific transport systems as pipelines, conveyors, carts or cranes are sufficient within a company. The transport systems are not the only variation in transport. The transported goods come in different shapes and sizes as well. Bulk solids, liquids, gasses and general cargo can be distinguished. In this research, the fo-cus will be on the transport of parcels, containers and general cargo within companies. In the previous century, with the advances in computer technology, the idea of automa-tion came up. It first started simple, with the steering system of a truck connected to a overhead steering wire. Then the wire became embedded in the floor with a cen-tral computer sending frequencies through wires, which could be detected by sensors on board the vehicles. Over the years, the systems became more and more advanced. The early autonomous systems were kept as simple as possible, because the computers were not able to do complicated calculations. With the advances of computer tech-nology, the computational burden is no longer a limiting factor [28]. Nowadays, au-tomation has become standard in internal transport and transport systems can function autonomously. This means that the transport systems do not need human interaction to fulfill their tasks [17]. Sensors are the eyes and ears of these autonomous trans-port systems, without sensors a transtrans-port system is not able to operate autonomously. Sensors can observe properties of the vehicle, its surroundings and obstacles. This

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in-formation is used to determine the next action of the system. This decisions can be corrupted by noise, uncertainty and disturbances on the entire system, the sensors or the actuators. The goal of this research is to identify the sources of noise, uncertainty and disturbances on the position estimation of autonomous transport systems, to find ways of measuring the influences of these undesirable effects and to describe methods for decreasing the effects of these noises, uncertainties and disturbances. Therefore three research questions are formulated:

• How are autonomous robots used for transportation purposes, and what are the differences for indoor and outdoor use?

• How do autonomous robots determine their location in the environment, which sensors are used and what are the sources of uncertainty?

• How can the data from multiple sensors be combined, to minimize uncertainty in position estimation of the autonomous robot?

As mentioned earlier, this research focusses on parcels, containers and general cargo. An overview will be made of present systems, both indoor and outdoor, to determine in which degree these systems are automated and gain insight in the working principles of these systems. This can help to identify the possible sources of noise, uncertainty and disturbance on the system. Then an overview is made of the sensors that are used in autonomous transport systems. A link is made between each sensor and the sources of noise, uncertainty and disturbance that are identified earlier. A comparison between the sensors is made. Then methods are discussed to combine data from different sensors, in order to use the strength of each sensor in order to reduce the uncertainty of the system as a whole. The report is structured in a similar way as the research questions. First an overview is made of the different autonomous transport systems that exist in the present in Chapter 2. Then the sources of uncertainty in general are identified in Chapter 3. In Chapter 4, the sensors used in autonomous transport systems will be discussed with the specific uncertainties that act on the sensors. Methods for combining data from different sensors are discussed in Chapter 5. Finally, in Chapter 6, conclusions are made and subjects for future research are proposed.

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Chapter 2

Autonomous transport systems

Since the 1950s, autonomous transport systems are used to make transport between production stations easier and more efficient. These systems differ in shape, size, max-imum load, etcetera. A distinction between autonomous transport vehicles for indoor use and for outdoor use can be made. Indoor systems consist mostly of light vehi-cles that operate between humans, while heavy outdoor vehivehi-cles operate in a special shielded area. In this chapter, the question ”How are autonomous robots used for transportation purposes, and what are the differences for indoor and outdoor use?” is answered for existing transport systems. The chapter is divided in three sections. Section 2.1 discusses indoor systems, Section 2.2 discusses outdoor systems and in Section 2.3, the similarities and differences between the indoor and outdoor systems are discussed. The uncertainty of the transport systems and the sensors used will be discussed in Chapter 3 and Chapter 4, respectively.

2.1 Indoor systems

Autonomous transport systems for indoor use are used in many different applications, for instance for box transport [22, 20, 68, 67], pallet transport [22, 21, 28, 68], trans-port of rolls of steel or paper [28], etcetera. For every use many different vehicles are developed, varying in shape, size and weight depending on the application. Some robots are fixed to a rail, others roam freely through the warehouse avoiding obsta-cles and other vehiobsta-cles. In this report the distinction is made between three types of autonomous indoor transport sytems: conveyor systems, automated guided vehicles (AGV) and automatic storage and retrieval systems (ASRS).

2.1.1 Conveyor systems

An efficient way of internal transport is by conveyor. If there are fixed routes for goods, conveyors are fast and can move large quantities between stations. Conveyors can move a wide range of products, from different sizes of boxes, crates and trays to pallets or even odd sized goods. Depending on the application, two conveyor systems can be

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(a) Belt conveyor with merge and bends [29] (b) Roller conveyor [67]

Figure 2.1: Examples of conveyor systems

Belt conveyor Roller conveyor

Maximum length ’no’ limit if divided in sections ’no’ limit if divided in sections

Maximum load 2000 kg 5000 kg

Maximum speed 0.5 m/s 0.2 m/s

Average througput Depends on the application

Table 2.1: Properties of conveyor systems

used: Belt conveyors or roller conveyors [67].

Belt conveyors are basically the same as those found in supermarkets, only for ware-housing they are bigger. Belt conveyors are used to transport boxes and crates from one process to another. Special belt conveyors exist for curves, diverts and merges [20, 67]. An example of a belt conveyor is given in figure 2.1(a). Roller conveyors are mostly used for heavy duty applications, for example pallet transport or other heavy or sharp items, but for boxes and crates as well. They are more resistant to heat, dirt, oil and other contaminants than belt conveyors [21]. As belt conveyors, roller conveyors can divert and merge and they can handle curves. An example of a roller conveyor is presented in Figure 2.1(b). Both types of conveyors work in a similar way, one or more motors drive the belt or the rollers to convey the items along the length of the conveyor. When no transport is needed, the conveyor stops and starts again if transport is needed. The conveyor keeps track of the items with a set of sensors at the beginning and at tactical points along the conveyor, for example before merges or exits. Conveyors are typically found in logistic centers, where large quantities of items need transport be-tween predetermined start and exit stations [67].

The length limit of a conveyor system is determined by the strength of the belt and the drive system. If the limit of a conveyor is 100 m and 200 m is needed, two convey-ors in line can be used. Therefore there is no limit to the length of a conveyor. The maximum load of a belt conveyor is 2000 kg and the maximum load of a pallet roller conveyor is 5000 kg per conveyor drive system [3]. Belt conveyors move faster than roller conveyors, at 0.5 m/s and 0.2 m/s respectively [3]. The average throughput of

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(a) Box runner (b) Pallet mover (c) Special carrier

Figure 2.2: Different AGV applications [28]

conveyors depends on the application, where the throughput of boxes is higher than the throughput of pallets or special items. The most important properties of conveyor systems are summarized in Table 2.1.

2.1.2 Automated guided vehicles

Automated guided vehicles (AGVs) are used for flexible transport between several sta-tions. Flexible means in context that the vehicles are to a certain degree, depending on the system, free to choose their route between the start and destination. An AGV is a driverless truck that is driven by an electric or LPG motor for indoor purposes [44]. Indoor AGVs can be used to transport anything, from boxes and crates, to pallets and even special goods like steel rolls. In some applications, for example the automobile industry, AGVs are even used as a moving platform on which the fabrication takes place [28]. Some applications of AGVs are shown in Figure 2.2. Figure 2.2(a) shows an AGV for tranport of boxes, Figure 2.2(b) shows a form of an AGV for pallet trans-port and in Figure 2.2(c) an AGV for transtrans-port of steel or paper rolls is shown. Basic AGVs in indoor applications were first used in the 1950s and evolved since then in highly intelligent vehicles. In the 1950s the first AGV system was built in grocery warehouse. It consisted of a modified truck and trailer following an overhead wire. In the 1970s, AGV systems became smarter. A frequency emitting wire was embedded in the floor to mark the path for the AGVs to follow. A floor controller turned the frequency wires on or off, guiding the vehicle to its destination. In the 1980s, when the processing power of computers grew, the AGVs became smarter and were able to compute their position and heading between fixed beacons in the environment. Nowa-days computers are fast enough to process images for positioning and navigation [28]. Todays AGVs have on-board computers and communication to receive assignments and to compute the actions needed to complete the assignments. The AGV then drives to the start, loads the goods, drives to the destination and unloads the goods. Several sensors are installed on the AGV to avoid obstacles, to find its way, to determine the position of the load, etcetera. AGVs are typically used in production or warehousing facilities with a lot of different loading and unloading stations [61]. Depending on the application, indoor AGVs can transport loads up to 30 tons [28]. Speeds are typically 1 m/s, but in some situations AGVs can reach speeds up to 4 m/s [61]. Depending on the type of battery and use of the AGV, a vehicle can work on average for 12 hours on

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(a) 3-wheeled AGV [43] (b) 4-wheeled AGV [28]

Figure 2.3: Different configurations of AGV wheels [28] Box AGV Pallet AGV Special AGV

Maximum load 100 kg 3 tons 30 tons

Maximum speed up to 4 m/s up to 4 m/s up to 1 m/s Run time on battery On average 12 hours

Charge time of battery On average 8 to 16 hours

Number of wheels 3 or 4

Type of wheels Solid rubber or synthetic material Table 2.2: Properties of indoor AGV systems

a fully loaded battery [61]. It takes 8 to 16 hours for such a battery to charge. Most indoor AGVs have three wheels (one for steering and driving, the other two for sup-port and stability), but four wheeled AGVs are used as well (two wheels for steering and driving, the other two for support and stability), see Figure 2.3(a) and 2.3(b). In most cases indoor AGVs use solid rubber or synthetic material wheels [28]. The most important properties of indoor AGVs are summarized in Table 2.2.

2.1.3 Automated storage and retrieval systems

Automatic storage and retrieval systems (ASRSs) are used to automate warehouse pro-cesses and to increase storage density and efficiency. These systems are used to trans-port units such as crates, boxes and pallets, but they are also suitable for transtrans-porting special items such as car tires [68].

Typically, with ASRSs the goods are stored in racks spanning the length and height of the warehouse. Between the racks, several systems exist for storing and retrieving goods, depending on the size and throughput.

One system relies on an automated crane on rails to store and retrieve the goods from the beginning of an isle to the rack and vice versa. Horizontally the entire crane moves

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Figure 2.4: ASRS crane and racks [2]

to the right position, while vertically a platform on the crane moves to the right level of the rack. The capacity of this system depends on the size of the isles and the speed of the crane [68]. This type of ASRS can handle a large diversity of items, from trays and boxes to pallets, and even special items. An example of this type of ASRS is given in Figure 2.4.

Another systems uses automated shuttles on tracks to transport the goods to and from the racks. In order to store items on multiple levels in the racks, elevators at the be-ginning of every isle lift the vehicle with the goods to the desired level. This system can achieve a higher throughput than the previous system, because the capacity of this system is primarily depending on the capacity of the lifts and the number of shuttles [68]. This ASRS is typically used for the storage and retrieval of trays and boxes. An example of this type of ASRS is given in Figure 2.5.

Both storage systems work in a similar way. An order is sent to the storage and re-trieval system to store or retrieve an item in the system. The exact storage location of this item is stored in a database. In the case that the item needs to be stored, the item is delivered to the right isle, where the ASRS picks it up and stores it in the reserved storage location. When an item needs to be retrieved, this process is reversed. Often a storage action is combined with a retrieval action, to minimize empty travel of the ASRS [2]. Sensors in the ASRS tell it what its location is and what the position of the

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Figure 2.5: Automated shuttle system [22]

Crane system Shuttle system

Length of rack ’no’ limit ’no’ limit

Height of rack Up to 50 m Up to 25 m

Maximum load 3000 kg 300 kg

Maximum speed horizontal 4 m/s 6 m/s Maximum speed vertical 0.5 m/s 1 m/s

Average throughput Up to 60 per hour Up to 600 per hour Table 2.3: Properties of ASRS systems

item is. ASRS are mostly found in large warehousing applications [22]. The length of an ASRS system is physically limited by the length of the warehouse it is built in, but besides from that fact, the length of the isles has no theoretical limit. For practi-cal reasons, the isles are kept relatively short in order to maintain the efficiency of the ASRS crane [68]. The height of the racks is basically limited by the strength of the posts of the racks. This limit is 50 m for the crane system and 25 m for the shuttle system. ASRSs can transport a maximum load of 3000 kg for the crane system and 300 kg for the shuttle system [22]. Maximum speeds can reach horizontally 4 m/s for the crane system and up to 6 m/s for the lighter shuttle system [22]. An ASRS crane can transport its load vertically with a speed up to 0.5 m/s, while the elevator for the shuttle system can reach 1 m/s vertically. The average throughput of the crane system is up to 60 units per hour. The throughput of the shuttle system can reach up to 600 units per hour [2].

The most important properties of ASRSs are summarized in Table 2.3.

2.2 Outdoor systems

Autonomous transport systems for outdoor use are in most cases found in harbor en-vironments, especially container ports. Nowadays large amounts of the transport of containers between ship and truck are automated using special vehicles, such as

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auto-Figure 2.6: AGV delivering a 40 foot container [6]

mated guided vehicles (AGVs), automated lifting vehicles (ALVs), automated stacking cranes (ASCs) and (partial) ship-to-shore cranes.

2.2.1 Automated guided vehicles

An outdoor application where AGVs are used often, is in container terminals. Their purpose is to transport ISO standard containers from quay cranes to the stack and vice versa. An example of an AGV can be seen in Figure 2.6.

When a container is unloaded from a ship by a quay crane, the AGV has to wait under the crane until it receives the container from the crane. The AGV drives over a prede-termined route from the quay crane to the stack, where another crane lifts the container from the AGV to the stack. Because an AGV cannot pick up a container by itself, the different cranes and AGVs have to wait for each other to deliver or receive a container. The number of AGVs needed depends on the capacity of the rest of the terminal and should be large enough, so that the AGVs are not restricting the capacity of the termi-nal.

To decouple the container transfer, lift AGVs are developed. Containers are placed in steel racks by the cranes, then the AGV drives between the racks under the container and lifts it just enough to take it off the rack. At the other end the AGV places the container on a rack and drives away and another crane can pick it up again [30]. An example of the lift AGV is presented in Figure 2.7.

The area where AGVs drive has a fence around it, to prevent humans interfering with the AGV. The AGV has a computer and a set of sensors on board to observe its po-sition, its surroundings and other vehicles or obstacles. The computer calculates the route between the locations where it receives and delivers the containers.

The properties of AGVs and lift AGVs are comparable. Both types of AGV are di-mensioned to transport 50 ft containers as well as 20 ft containers. The vehicle is approximately 18 m long (bumper to bumper), 3 m wide and 1,5 m high. The top of the AGV is flat over the entire length. The motor, fuel tank, computers and other components are located under the loading platform. An AGV has an own mass of 17.5

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Figure 2.7: A lift AGV and container rack [49]

(Lift) Automated guided vehicle Mass of the vehicle Approximately 19 tons

Maximum load 60 tons Maximum speed 6 m/s

Energy source Diesel-hydraulic, diesel-electric or fully electric Fuel consumption 8 l/hr

Number of wheels 4

Type of wheels Inflatable rubber tires

Table 2.4: Properties of outdoor AGV systems

tons and carries 1 ton of diesel and 0.5 ton of hydraulic oil in case of a diesel hydraulic powered vehicle [23].

Recently, completely battery driven AGVs are developed. These AGVs carry a battery pack which ensures 12 hours of full operation. Then the AGV drives to an automated battery station, where the empty battery is replaced for a charged one [48].

The AGVs drive on four inflatable rubber tires with speeds up to 6 m/s, consuming about 8 liters of fuel per hour [30]. They can transport a single 40 ft container with a mass up to 40 tons or two 20 ft containers with a combined mass up to 60 tons. Several manufacturers fabricate AGVs, but the most important properties of different (lift) AGVs are similar, those are summarized in Table 2.4.

Over the years, with increasing processing power of on board computers, AGV have become smarter and the positioning and navigation techniques changed.

2.2.2 Automated lifting vehicles

Automated lifting vehicles (ALVs) are special AGVs. ALVs are also used for container transport from a quay crane to the stack, but a large advantage of ALVs over AGVs is that the first can pick up containers from the ground. Therefore the container transfer from crane to ALV is decoupled and less vehicles are needed to maintain the same production, because areas under the crane can be used as buffer areas during peak

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Figure 2.8: Fully automated one-over-one ALV of Kalmar [14]

Automated lifting vehicle Mass of the vehicle Approximately 65 tons Maximum load 50 tons

Maximum speed 8 m/s Fuel consumption 10 l/hr Number of wheels 6 to 8

Type of wheels Inflatable rubber tires Table 2.5: Properties of ALV systems

operation [14]. An example of an ALV is shown in Figure 2.8. The environment in which ALVs operate is similar to the environment of AGVs, a secluded area in which no people are allowed during operation of the vehicles. Containers are placed in lanes underneath the quay crane or at the end of a stack, where the ALV drives over the container and lifts it. The vehicle then drives to the destination of the container, puts it on the ground and drives away. The set of sensors and on-board computer are comparable to the AGV, the vehicle observes its position, its surroundings and other vehicles or obstacles. The computer calculates the route between the locations where it receives and delivers the containers. The number of ALVs needed depends on the application, but should be designed such, that the capacity of the container terminal is not restricted by the number of ALVs. The properties of an ALV are similar to those of the AGV. The ALV can transport one 40 ft container with a mass up to 40 tons or two 20 ft containers in twin lift with a mass up to 50 tons. The maximum speed of the vehicle is 8 m/s on 6 to 8 inflatable rubber tires, consuming about 10 liters of fuel per hour [14, 30, 56]. The mass of an ALV is substantially higher than an AGV, approximately 65 tons, but the vehicle is with 10 meters also substantially higher [24].

Several manufactures fabricate ALVs, but the most important properties of different ALVs are similar, those are summarized in Table 2.5.

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Figure 2.9: Two ASCs per stack [38]

2.2.3 Automated stacking cranes

Automated stacking cranes (ASCs) are rail mounted portal cranes, designed to store and retrieve containers in the stacking yard of a container terminal. One stack of con-tainers is operated by one or two ASCs. In some applications the ASCs are designed to be able to pass each other, most of the time they cannot pass each other. Then one crane is used to store and retrieve containers for AGVs at seaside, the other is used to store and retrieve containers for trucks at landside of the terminal. An example of two ASCs over a stack is shown in Figure 2.9.

ASCs are used for automatic stacking and retrieving of containers in the stacks. The stacks are typically located between the landside and the waterside of the terminal. An ASC driving over a stack can transport containers both to the landside (one end of the stack) and to the portside (the other end of the stack). When a container is delivered to a stack, on the landside or the portside, the vehicle carrying the container parks in line with the stack. There the ASC is able to lift the container from the vehicle and transport it into a position in the stack, that position is then stored into a database. When a container needs to be retrieved from a stack, the position is determined from the database and the ASC picks up the container from that position and delivers it to the vehicle waiting at the end of the stack. Sensors on the ASC measure the position of the ASC in the stack and the exact location of the containers to pick them up. Working areas of ASCs are shielded from the rest of the terminal, to prevent people walking in between the ASCs [1]. ASCs can transport all sizes of containers that are transported by ship. A typical stack under an ASC is 6 to 10 containers wide and 5 containers high. The crane can lift 6 containers high to be able to transport containers over the stack. ASCs must be able to handle all containers from ship to shore and vice versa, so they are designed to transport up to 50 tons. The crane can travel 4.5 m/s over its rails, the trolley can travel 1 m/s and the crane can hoist a container with speeds up to 1.5 m/s, depending on the mass of the container it is lifting [13]. The crane has steel wheels, driven by electric motors.

Several types of ASCs exist, but the most important properties of the different ASCs are similar, those are summarized in Table 2.6.

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Automated stacking crane Rail span 6 to 10 containers

Stacking height 1 over 5 containers Maximum load 50 tons

Gantry speed Up to 4.5 m/s Trolley speed 1 m/s Hoisting speed Up to 1.5 m/s Energy source Electric Type of wheels Solid steel

Table 2.6: Properties of ASC systems

Figure 2.10: Secondary trolley of a STS crane, with in the background an entire crane [70]

2.2.4 Ship-to-shore cranes

Ship-to-shore (STS) cranes or quay cranes transport goods or containers between the ship and the quay. STS cranes are untill today not fully automated, but only semi-automated due to safety restrictions. Picking up and placing the container is still done by an operator on the crane, while the trajectory between the ship and the shore is au-tomated [1]. However, when the rest of the terminal is auau-tomated, the STS cranes and the skills of their operators become the bottleneck in the ship loading and unloading process [76].

Difficulties in automating these cranes are for example the variety of ships they have to serve, changing tides and therefore the height of the ship, swinging of the spreader, different sizes of containers and weather conditions [1]. To overcome these difficul-ties, some STS cranes are partially automated. They have a manned main trolley to exchange containers between the ship and a platform on the crane. Then an automated trolley exchanges the containers between the platform and transport equipment in the terminal. Research is being done for fully automating the entire STS crane as well [76]. As mentioned are modern STS cranes semi-automated. The STS crane automatically

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Ship-to-shore crane Maximum load 70 tons

Gantry speed Up to 1 m/s Trolley speed Up to 4 m/s Hoisting speed Up to 3 m/s Energy source Electric Type of wheels Solid steel

Table 2.7: Properties of STS cranes

moves the spreader to within meters of the target container. There the operator takes over and moves the spreader the last few meters until he is able to connect it to the container. Then the crane moves the container automatically to within a few meters of the destination, where the operator again takes over to move the container the last me-ters and disconnect it. The computer on board of the crane calculates the most efficient trajectory [1]. In Figure 2.10, a detail of a STS crane with a secondary trolley is shown, with in the background the entire crane with two trolleys.

Where a manned STS crane can do about 30 moves per hour, automated STS cranes should be capable of doing up to 55 moves per hour [76]. An STS crane can travel along the quay with up to 1 m/s, its trolley can travel with speeds up to 4 m/s and it can hoist a load of 70 tons with approximately 1 m/s. The maximum hoisting speed is 3 m/s. All drives are electrical [59].

A summary of the most important properties of the STS cranes are presented in Ta-ble 2.7.

2.3 Similarities and differences between indoor and

out-door systems

Automated transport systems are used in many different applications, as is shown in the previous paragraphs. There is a different system for every application, but there are similarities and differences between every one of them. In this report the systems are divided into two groups: indoor systems and outdoor systems, because within these groups systems show the largest similarities. Indoor systems are especially used to transport small goods, from boxes to pallets, between work stations in a warehousing or production environment. Most systems transport loads up to 3 tons with speeds up to 4 m/s, while special transport systems take loads up to 30 tons. All indoor systems are driven by LPG or electric motors and have solid rubber or synthetic wheels. Indoor systems work most of the time in environments where humans work and walk as well. Outdoor systems are especially used for transport of containers in terminals, from the ship to the road or rail and vice versa. Regular loads can be as much as 60 tons, trans-ported at speeds up to 8 m/s. More and more research is done to make outdoor systems fully electric, but most are still diesel-hydraulic or diesel-electric. Depending on the vehicle, it is mounted on solid steel wheels for driving on rails or on inflatable rubber

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(a) Automated container terminal [30] (b) Indoor AGV system [46]

Figure 2.11: Outdoor and indoor application of an AGV system

tires in other applications. Working areas of automated outdoor systems are shielded from humans as much as possible.

The biggest differences between indoor and outdoor systems are the size of the vehi-cles, transported loads, speeds, energy source and structure of their working area. In Figure 2.11 an outdoor AGV environment and an indoor AGV environment are shown. The main properties of indoor and outdoor systems are summarized in Table 2.8. The systems are similar in the way they use sensors for positioning, recognition of the target object and observation of the surroundings, other vehicles and obstacles. The sensor readings are subject to noise and uncertainty, and varying circumstances in the sur-roundings may cause uncertainty as well. The possible uncertainties will be discussed in Chapter 3, the sensors that can be used in autonomous transport systems will be discussed in Chapter 4. In the further course of this research, only automated guided vehicles will be discussed. These vehicles are most interesting, because they are able to move freely through their working area which leads to different sources of noise, uncertainty and disturbances.

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Indoor systems Outdoor systems Box transport P allet transport Special transport Container transport Maximum load 300 kg 3 tons 30 tons 60 tons Maximum speed 6 m/s 4 m/s 1 m/s 8m/s Ener gy source LPG or electric Diesel-h ydraulic, diesel-electric or electric W orking area In between humans Secluded area Wheel material Solid rubber or synthetic material Inflatable rubber or solid steel T able 2.8: Comparison between indoor and outdoor systems

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2.4 Summary

Autonomous transport systems come in many varieties, both for indoor as well as out-door applications. Inout-door systems are used to transport a wide range of items, from boxes to pallets or even special items as steel rolls. For every application, a suit-able transport system is required. For indoor use, belt or roller conveyors are used to transport boxes or pallets between working stations. Once installed, the possible routes cannot be easily changed. Another indoor system for transport between work-ing stations are automated guided vehicles (AGVs). AGVs exist in different forms, to transport boxes, pallets or special items. The advantage of AGVs is that routes can be changed after installation of the system. For storage applications, automated stor-age and retrieval systems (ASRS) can be applied. These system are able to store and retrieve boxes, pallets or special items automatically in high warehouses. Outdoor sys-tems are often used in container terminals. Automated guided vehicles (AGVs) are used to transport different sizes of containers between the stacks and the quay cranes. AGVs cannot load and unload containers themselves, but some special AGVs, called lift AGVs are able to (un)load containers in specially designed racks. Another type of autonomous outdoor transport system is the automated lifting vehicle (ALV). This sys-tem has the same task as the AGVs, but the advantage of the ALV is that it can (un)load containers from the ground. Automated stacking cranes (ASC) are used for the auto-mated transport of containers to and from the stacks, both on portside and landside. Ship-to-shore cranes or quay cranes are used to (un)load ships. These cranes are only semi-automated. Indoor and outdoor systems mainly differ in size, maximum load, maximum speed, energy source and working area, but they are similar in the use of sensors.

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Chapter 3

Uncertainty in position

estimation

In Chapter 2, the different autonomous transport systems for indoor and outdoor use are discussed. This chapter goes into the details of uncertainty that can affect the performance of the transport systems. This chapter answers the question: ”What are the sources of uncertainty in automated guided vehicle applications?”. The reason this is a separate chapter is that one source of uncertainty can influence more than one aspect of the localization and sensors. In Chapter 4, the sensors used in automated guided vehicles and the effect of the uncertainties on the sensors will be discussed. Figure 3.1 presents a schematic overview of the position estimation process of an AGV. A controller, either on board or central, sends signals to the actuators of the vehicle. These actuators are affected by noise and uncertainty, the action of the actuator has a certain bandwith around the value desired by the controller. The actuator noise and uncertainty will be discussed in Section 3.1. The entire vehicle is subjected to external influences, such as road conditions, which will be discussed in Section 3.2. Several sensors measure the new position of the vehicle, with noise and uncertainty in the measurements. This sensor noise and uncertainty will be discussed in Section 3.3. The sensors send signals to a filter, either on-board or at a central location. In the last case, communication is needed, which is discussed in Section 3.4. The filter is used to fuse the signals from different sensors into a position estimation, from which the controller can determine the next action. The quality of the filter will be discussed in Section 3.5. At the end of this chapter, in Section 3.6 a comparison is made between the effects of the different sources of uncertainty on both indoor and outdoor systems.

3.1 Actuator noise and uncertainty

Actuators are the cause of movement of the transport system. An actuator receives a task from a controller and executes this task as good as possible. However, insufficient accuracy or disturbances from the surroundings can adversely affect the actuator’s per-formance of the task.

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Figure 3.1: Overview of an automated system with noise, uncertainties, disturbances and delays

3.1.1 Wheel slip

Wheel slip or spin can occur if to much power is sent to the wheels of a vehicle, if the vehicle is braking hard or if the road surface is slippery. In all three cases, the motion force of the wheels overcomes the friction force between the wheels and road surface, resulting in slipping of one or more wheels of the vehicle. Wheel slip occurs in indoor applications as well as outdoor applications. In indoor applications, dust and filth can reduce friction of the floors. Outdoor applications are more prone to wheel slip, because the friction of the road surface is not only influenced by dirt and sand, but also by the weather (rain, snow, ice). The result of wheel slip is that distance measurements, calculated from wheel revolutions, become unreliable [11, 23, 63].

3.1.2 Side drift

Side drift is the phenomenon that a moving vehicle deviates from its planned course in a sideways direction. It can be caused by two reasons: Wheel deflections while driving through a corner and wind. While driving through a corner, rubber tires deflect because the vehicle wants to remain straight motion, resulting in a wider corner than the planned course. With substantial side wind, the vehicle is blown sideways of its planned course due to deflections of the tires in motion [12, 41]. In Figure 3.2, a visual representation is given for the two types of side drift. Position (a.) is the starting position, position (b.) is the position after driving the planned course and position (c.) is the position after driving the planned course with side drift. In the case of side drift, the orientation

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(a) Side drift due to navigating a corner (b) Side drift due to wind

Figure 3.2: Effect of side drift

of the steering wheels of the vehicle does not represent the trajectory travelled by the vehicle.

3.1.3 Substantial load variations

Autonomous transport vehicles must be able to perform at maximum load, but travel sometimes empty as well. Especially container AGVs are working with substatial load variations - about 20 tons empty to 80 tons at maximum load [23]. With these variations in load, the diameter of the inflatable tires decreases under increasing load [64]. In the case that the diameter of all wheels decreases with the same amount, the vehicle travels the same course as planned, but a shorter distance at the same amount of wheel revolutions. In case of an excentric load, the vehicle will turn because the diameter of the tires at one side of the vehicle is smaller than the other side, and therefore the distance travelled at the same amount of wheel revolutions. For instance if the left side of the vehicle is heavier than the right side, the vehicle will make a left turn. In Figure 3.3(a) the effect of an empty vehicle (b.) versus a vehicle with maximum load (c.) is shown. In Figure 3.3(b) the effect of an evenly loaded vehicle (b.) versus an unevenly loaded vehicle is shown. Due to unevenly distributed loads, the orientation of the steering wheels and the number of revolutions do not represent the true trajectory travelled by the vehicle.

3.1.4 Tire wear

Due to the extensive use of automated vehicles, over time significant tire wear can become a problem. The diameter of the tires decreases over time, causing the same

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(a) Evenly distributed load (b) Unevenly distributed load

Figure 3.3: Effect of a load on the course of the vehicle

problems as stated in the previous section. Especially for AGV applications, where vehicles navigate over standard (counter)clockwise routes, the tires at the outer side of the circle will wear faster than the ones on the inner side of the corners [63]. See Figure 3.4 for a typical layout of a container terminal, in this case the Euromax terminal in Rotterdam. AGVs can cross lanes between the ship-to-shore cranes, but underneath the cranes all AGVs move to the left and at the stacks all AGVs move to the right, as indicated by the green arrows.

3.2 System disturbances

Automated systems are used to replace humans in certain processes, in order to make these processes faster and more efficient. Therefore automated systems must be able to perform in the same environmental conditions as manned vehicles. Requirements for automated vehicles are operation both day and night, in all weather conditions and on rough terrain. These requirements can pose difficulties for position estimation and navigation.

3.2.1 Weather influences

Weather influences are a major issue for automated transport. It can affect the perfor-mance of automated vehicles for outdoor use in a negative way. Rain, snow and ice change the properties of the road surface, the more slippery surface can result in wheel slip and side drift [12, 23]. Not only road vehicles are subject to bad weather, vehicles on rails can be influenced as well, because the rails get slippery due to rain or ice.

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Figure 3.4: Overview of a typical container terminal with driving directions

3.2.2 Uneven road surface

An automated transport system, especially when used outdoors, can be disturbed by irregularities and small obstacles on the road surface [23]. Irregularities can be potholes in the road surface, but also tracks formed due to vehicles driving the same route over and over. These irregularities can cause the AGV to wander off the planned course. Small obstacles can be debris, but also sand or gravel. These small objects are not detected by the AGV, but they can cause wheel slip or side drift in corners, resulting in a different course than planned.

3.2.3 Collisions

Another disturbance, is a collision with another vehicle or object. Adequate sensors and vehicle control should prevent collisions from happening, but when something fails the result can be catastrophic. On the other hand, vehicles preventing a collision can disturb the system as well in the form of a deadlock. In order to prevent a collision, both vehicles stop and keep waiting for the other to continue. A special disturbance for AGVs is interaction with humans in the same area. Humans are unpredictable and vehicles may need to stop for people crossing their path.

3.3 Sensor noise and uncertainty

In order to let automated transport systems drive autonomously, several sensors are needed to ensure safe, adequate and efficient behaviour. The right choice of sensors, ensuring a high enough data rate or resolution, can be beneficial to reduce the initial uncertainty. However, the signals received by these sensors are subjected to noise

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and disturbances from the surroundings, resulting in uncertainties in the signal of the sensor.

3.3.1 Weather influences

The weather can influence the performance of sensors in different ways. Sensors re-lying on the number of wheel revolutions or the angle of the wheels can have great uncertainty when the road surface is covered with snow or ice. The vehicle can have wheel slip or side drift in corners, without the sensors noticing it. Other sensors, such as vision based sensors, can have great uncertainty when fog, snow or heavy rain blocks the sight of these sensors. In this case, with only vision based sensors, the vehicle cannot determine its location from the surroundings [23].

3.3.2 Signal blockage

Objects or structures in the surroundings of an AGV can obstruct the signal from its sensors. Indoors, objects such as racks, pallets or other vehicles can block the view of the sensors of an AGV. Outdoors, objects could be ship-to-shore cranes, other vehicles or buildings. Both indoors and outdoors this causes a problem for position estimation with these sensors [23, 63].

3.4 Communication

Communication is essential for the reliability of the system. Messages to and from the vehicle contain for example information about where to go, when to start, which speed to drive and where to make corners. If this information is received too late or not at all regularly, the system is very unreliable. There are essentially four types of basic communication being used with autonomous vehicles: radio, infrared, guide wire data and inductive loops communication.

3.4.1 Radio communication

Radio communication provides a continuous two-way link between a base station and the different autonomous vehicles, if the entire workspace is covered by antennas. Ra-dio communication enables on-the-fly programming of vehicles. It enables a vehicle to download new maps or routing information quickly and it can send information con-taining vehicle ID, location, load status and traffic condition requests. Radio provides constant communication between the vehicles and the base station and creates a reli-able system of very responsive vehicles, that can quickly react to changing working conditions. Interruptions of the radio signal can be caused by interference of the radio signal or the vehicle driving out of range of the antennas [28].

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3.4.2 Infrared communication

Infrared is a point-to-point communication method. A vehicle must stop in front of a sensor in order to receive the complete data stream. Usually these data exchange points are placed at (un)loading or charging stations. The data transfer can be unsuccessful if the connection is lost to early or if the sender and receiver do not line up. Because infrared communication is not continuous, it is not used any more for communication between the vehicles and the base station [28].

3.4.3 Guide wire data communication

Data communication through guide wires buried in the floor gives the same flexibility as radio communication, given that the vehicle stays in the direct proximity of the guide wire. Since the distance from the on board receiver to the wire is constant, a continuous data stream to and from the vehicle can be established. However, this technique is not used much any more, because guide wire transport systems are replaced with more modern systems [28].

3.4.4 Inductive loops communication

Inductive loops are another point-to-point data transfer method. Wire loops, 1 to 3 meters long, are buried in the floor at locations where communication with the vehicle is desired. Simple messages can be transferred to and from antennas at the bottom of the vehicle while it drives over the loop. This system can fail if the vehicle does not drive completely over the induction loop or if the vehicle passes the loop too quick, resulting in an incomplete data transfer. Although this is a very inexpensive technique, it is a very limited method of data transfer [28].

3.5 Quality of the data fusion

The last step in the position estimation of an autonomous vehicle is also the most important step. The vehicle relies on multiple sensors to estimate its position, and each sensor supplies data at a different rate, in a different format (i.e. angle of the wheels, distance travelled), etc. The function of the data fusion algorithm is to combine all the different data sets into one position estimate, in order to minimize the uncertainty of the separate sets. The quality of the algorithm determines the final uncertainty of the position estimation of the system. The different fusion algorithms and their advantages and disadvantages will be further discussed in Chapter 5.

3.6 Similarities and differences in uncertainty between

indoor and outdoor applications

Automated guided vehicles for indoor or outdoor use are mostly subjected to the same disturbances and uncertainties. In Table 3.1 a summary is presented of the influence of

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Disturbance Indoor system Outdoor system

Wheel slip 0 +

Side drift 0 +

Substantial load variations + +

Tire wear 0 +

Weather influences - +

Uneven road surface 0 +

Collision + 0

Signal blockage + 0

Table 3.1: Influence of disturbances on indoor and outdoor systems

the different disturbances on indoor and outdoor vehicles, as will be elaborated below. In the table ’0’ means average, ’-’ means less than average and ’+’ means more than average. Wheel slip and side drift are more likely to occur outdoors, because sand can easily blow over the road surface. Indoors dust can collect on the floors. Both indoor and outdoor systems cope with substantial load variations, from driving empty to transporting maximum load. Tires of outdoor vehicles will wear faster than those of indoor vehicles, because of the mass of the vehicles and the rougher road surface outdoors. The weather can influence uncertainties in outdoor vehicles in several ways, as stated in the previous sections, while indoor vehicles are completely shielded from weather influences. Outdoor vehicles have to drive over a rougher terrain than indoor vehicles that drive over leveled surfaces. Indoor AGVs have more risk of collisions than outdoor vehicles, because of the tight spaces they are navigating and the people in their surroundings. Signal blockage is also a bigger problem for indoor vehicles, because more obstacles are found in indoor areas.

3.7 Summary

Uncertainty can occur at every part of an autonomous vehicle, both for indoor use as well as for outdoor use. In this chapter it is shown that not only the actuators, sensors and communication can be affected by noise and uncertainty, but that disturbances on the system as a whole or the way of combining data from different sensors can influence the uncertainty of the vehicle as well. Actuators can be influenced by the spinning of wheels, side drift, substantial load variations or tire wear. The vehicle can be disturbed by the weather (slippery road surface), bumps or holes in the road surface or collisions. Uncertainty in sensor readings can be caused by weather influences (slipping wheels or limited vision) or signal blockage. Interrupted communication can cause incomplete data transfer to and from the vehicle. The quality of the data fusion can either reduce the uncertainty of the vehicle if it is done properly or enlarge the uncertainty if it is not done properly. Some of these uncertainties apply more to outdoor systems than to indoor systems and vice versa.

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Chapter 4

Sensors for positioning and the

effect of uncertainties

Autonomous vehicles observe their surroundings through sensors. There are sensors solely for positioning or heading, but some sensors are capable of doing both. For ac-curate positioning, it is shown in this chapter that the autonomous vehicle should have different sensors.

This chapter answers the question: ”How do autonomous robots determine their loca-tion in the environment and which sensors are used?”. Different sensors have different methods for position measurements, therefore it depends on the type of sensor how the position estimate is calculated. In this chapter, different sensors for positioning and navigation are presented, and the localization method that comes with it. There are many sensors on the market for positioning and navigation. They differ in application, computational power required, accuracy and price. There are basically two methods for autonomous vehicle positioning: relative position measurements and absolute position measurements. These methods differ mainly in the way the measurement error grows over time. In the next sections, the two methods and their sensors will be discussed.

4.1 Relative position measurements

Relative positioning uses a known starting position and heading, and calculates an estimate of its next position through the motion of the vehicle from the starting position. There are two methods dIscussed in this section, odometry and inertial navigation.

4.1.1 Encoders

Odometry is the study of position estimation during wheeled vehicle navigation. The word odometry is composed of the Greek words hodos (travel or journey) and metron (measure) [71]. It uses encoders on the wheels to measure the number of revolutions and the steering angle of the wheels. Odometry is the most widely used method for determining real time position estimation of autonomous vehicles [12].

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(a) Cornering (b) Straight path with two 90 degree cor-ners

Figure 4.1: Odometry error accumulation [50]

Encoders are inexpensive and in most autonomous vehicles they are also used for con-trolling the speed of the vehicle [28]. Encoders use little computational power, because of the simplicity of the system. Therefore, position estimations based on encoders can be done at very high sampling rates [45]. The biggest advantages of odometry are that it is a self contained system, which can always provide the vehicle with an estimation of it’s position [9]. In order to be able to calculate the position of the vehicle after some travel from a known starting point, the encoder readings have to be translated in heading and distance. This is based on simple equations, which hold true when wheel revolutions can accurately be translated to linear displacement [10]. The only information required is the wheel diameter. The odometry equations rely on the fact that wheel revolutions can be translated into a displacement of the vehicle. However, encoder readings can be disturbed in several ways, as described in Chapter 3. These disturbances can be grouped in systematic or nonsystematic errors. Systematic errors are specific for the vehicle itself. These errors can be caused by unequal wheel di-ameters, uncertainty about the effective wheelbase (because the wheels have not one point of contact with the floor), misalignment of wheels, limited encoder resolution and limited encoder sampling rate. Especially the first two disturbances are notorious systematic errors [12, 10]. Nonsystematic errors are caused by the surroundings of the vehicle. These disturbances could be bumpy or uneven floors, unexpected obstacles on the floor, wheel slippage, side drift or collisions [12]. Odometry has one big disadvan-tage, the errors accumulate unbounded over time. Especially uncertainty in orientation measurements can cause serious deviations from the planned path. Odometry errors can grow so fast that the position estimation will be totally wrong after as little as ten meters [12]. The accumulation of errors is represented graphically in Figure 4.1. The uncertainty of the position estimation by odometry has an elliptical shape, because the lateral uncertainty is bigger than the uncertainty in the driving direction. The lateral uncertainty can be a result of systematic errors, such as unequal wheel diameters, caus-ing the lateral uncertainty to grow unbounded. The uncertainty in wheel slip can be the result of nonsystematic errors, such as wheel slip, causing the actual driven trajectory to be shorter or longer than the planned trajectory. As mentioned, systematic errors

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have a higher impact on the uncertainty than nonsystematic errors [12, 10].

In order to keep the odometry errors small, the position estimates need to be reset by absolute position measurements on a regular basis.

4.1.2 Inertial navigation

Inertial navigation is used for a long time in aircraft and spacecraft for measuring and controlling the position and heading. It uses accelerations as a basis for position and heading estimation. For linear movement in x, y and z direction, accelerometers are used to measure small accelerations of the vehicle. Gyroscopes are used for measuring accelerations in orientation [9].

Until the nineties, accurate gyroscopes were very expensive and only used in aircraft and spacecraft. However, during the nineties, fiber-optic gyroscopes (also called laser gyros) have fallen dramatically in price. These laser gyros are known to be very accu-rate, became a good solution for autonomous vehicle navigation [10].

As odometry, because of the simplicity of inertial navigation the computational power required is very low, and real time data can be calculated at very high sampling rates. The inertial sensors are self contained, non-radiating and non-jammable, resulting in a system which can always provide the vehicle with position data [9]. The data from the accelerometers and gyroscopes consists of accelerations. In order to determine the speed of the vehicle, the data has to be integrated once. To determine the position estimate of the vehicle, the data from the sensors has to be integrated twice [45]. A disadvantage of inertial sensors is that they suffer from extensive drift with time. Be-cause of the need to integrate the sensor data to obtain the position estimate, any small constant error can increase unbound after integration. Therefore, inertial navigation is not suitable as sole system for position estimation over extended periods of time [9]. Especially accelerometers are sensitive for irregularities. A bump in the road surface translates into a gravitational acceleration, resulting in a false position estimate. This problem may be overcome by implementing a tilt sensor, but the drift will still be high [12]. Research showed that a tilt compensated system still has a position drift rate of 1 to 8 cm/s, depending on the frequency of acceleration changes [9]. Moreover, it is difficult to distinguish the measured signal from the sensor noise at low accelerations for both the accelerometer and gyroscope [9]. Despite the disadvantages, gyroscopes can help to detect and correct the orientation error of odometry immediatly [9].

4.2 Absolute position measurements

Absolute positioning can calculate its position without knowledge of its starting posi-tion. Unique beacons are placed in the working area of the vehicle, that can be rec-ognized with on board sensors. With the positions of the unique beacons known, the vehicle can calculate its position using trilateration or triangulation.

Trilateration uses the distances to the beacons to calculate its position. With three bea-cons detectable, the exact location of the vehicle can be calculated, see Figure 4.2(a). Triangulation uses angles with the beacons to calculate its position. With the posi-tions and angles of two beacons known, the exact location of the vehicle can be

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cal-(a) Trilateration (b) Triangulation

Figure 4.2: The principles of trilateration and triangulation

culated, see Figure 4.2(b). In this section nine different absolute positioning methods are discussed: guide wire, touch, magnetic compass, cartesian guidance, radar, laser, ultrasonics, vision based positioning and the global positioning system.

4.2.1 Guide wire

In the 1950s the first automated guided vehicles were used. Basically they were mod-ified trucks and trailers following a physical overhead wire. In the 1970s, the vehicles became smarter. An onboard antenna was programmed to follow a specific frequency, emitted by a wire embedded in the floor. A floor controller turned the frequency wires on or off, guiding the vehicle to its destination. Because the intelligence of these sys-tems is with the floor controller and not in the vehicles, this system is described as ’smart floors, dumb vehicles’ [28].

A guide wire system is also described as fixed path navigation. The entire route of the vehicle from one station to another needs to be physically linked by a wire. For every route, a separate wire must be installed. If a route needs to be changed, operations on the floor need to be stopped in order to install a new wire on the new route.

Several types of fixed path exist [28, 43]:

• A narrow magnetic tape on the floor surface

• A narrow photo sensitive chemical strip on the floor surface • A narrow photo reflective tape on the floor surface

• A frequency emitting wire buried just below the floor surface

Sensors on the vehicles detect these fixed paths and guide the vehicle along the route. The first three types of fixed paths require sensors on the underside of the vehicle to detect the surface mounted wire. If the wire makes a turn, the sensors detect it and

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(a) Wire buried in the floor (b) Vehicle with wire guidance antenna

Figure 4.3: Wire guidance system [16]

send a signal to the vehicles controller to make a turn and keep following the wire. The last type requires two antennas at the bottom of the vehicle to detect the magnetic field surrounding the embedded wire, see Figure 4.3. If the vehicle is centered above the wire, both antennas receive an equal signal. When the wire makes a turn, one antenna gets a stronger signal than the other and a control signal is sent to the vehicle controller to make a turn and follow the wire until both signals are again equally strong. The guidance signals emitted by the wire are often disturbed by rebar or other electronic signals. [28].

Nowadays, with more computational power on board and smarter vehicles, wire guid-ance is not used much any more [28, 36].

4.2.2 Touch

Touch is the most basic form of obstacle detection. It is only used as navigation sen-sor in small mobile devices, such as an automated cleaning robot, see Figure 4.4(a). In large autonomous vehicles, touch is only used as final level of safety. If all the other sensors on board fail to detect an obstacle, the touch sensors in the bumper (see Figure 4.4(b) stop the vehicle immediately on impact [24, 28].

4.2.3 Magnetic compass

In the previous paragraphs is shown that vehicle orientation is important for accurately estimating the position. Both odometry and inertial navigation experience serious un-certainty in orientation measurements. A sensor that can provide the vehicle with ab-solute heading information, is a magnetic compass. Several varieties of magnetic com-passes exist, but the compass best suited for autonomous vehicle applications is the fluxgate compass, see Figure 4.5 [10]. The magnetic compass is a low cost, self

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con-(a) Autonomous cleaning device [69] (b) Bumper of an automated lifting vehi-cle [24]

Figure 4.4: Touch sensor applications in autonomous systems

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Figure 4.6: Transponder in the road surface and sensor bar underneath a container AGV [30]

tained sensor, which can give high sample rates. The compass needs to be kept level to be able to measure the horizontal component of the earth’s magnetic field. If the vehicle is expected to drive over bumpy or uneven road surfaces, the compass should be placed in a special mount to keep it level at all times to prevent errors from measur-ing also the vertical component of the earth’s magnetic field. One disadvantage of the magnetic compass is that the earth’s magnetic field can be distorted by steel structures or power lines [10].

4.2.4 Cartesian guidance

Cartesian guidance uses a grid of transponders covering the entire floor surface of the area where autonomous vehicles would drive. The transponders alone cannot provide enough data to a vehicle to provide reliable navigation, unless the grid of transponders is so dense that it resembles wire guidance. However, a grid of transponders is often combined with odometry or inertial navigation to minimize the uncertainty of these position estimation methods.

The transponders can be small magnets or RFID tags embedded in the road surface [28, 62]. The transponders are relatively cheap, but to embed them in a grid pattern in the floor may raise the cost of this system. An autonomous vehicle knows the positions of the transponders in the working area, so whenever it detects a transponder, the vehicle knows its exact position. In between the transponders, the vehicle relies on odometry [43]. In order to ensure the vehicle driving over the transponders, the grid must be dense enough to compensate for the odometric uncertainty. As an added measure, not one sensor is mounted under the vehicle, but a vehicle wide sensor bar (Figure 4.6 [28]. This sensor bar detects the offset of the transponder from the center of the vehicle and sends a control signal to correct it.

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Figure 4.7: Overview of the radar system in its simplest form [55]

4.2.5 Radar

Radar is short for RAdio Detection And Ranging. It uses radio waves to detect mov-ing objects, but also weather formations and the shapes of its surroundmov-ings. For au-tonomous vehicles, especially the first and last are important. The radar sensor can be used to detect the shape of the surroundings and match the information with a pre-built map stored in the vehicles on-board computer, but the sensor can also be used for ob-stacle and collision avoidance.

Radar was first used in 1904 for ship detection, with a range of 3 km. In the years before World War II, development continued at a faster pace. The English developed a radar system to get an early warning system for the German Luftwaffe, while the German developed radar to warn them for the superior English naval fleet [55]. Radar is compared to the previous absolute positioning methods more expensive, but because it can be used for more than guidance alone, the sensor is still relatively cheap [54]. The obstacle avoidance costs little computational power, but the matching of the radar image to the pre-built map needs more calculations [24].

The principle of radar is relatively simple. A signal generator sends a specific fre-quency through a rotating antenna into the air. When objects are within range, the signal bounces back and is detected by a receiver/processor unit in the radar. The time difference between sending and receiving and the orientation of the receiver when the signal came back determine the exact location of the detected object. A schematic overview of the radar in its simplest form is given in Figure 4.7 [55]. In normal opera-tion a radar will obtain 4 to 10 observaopera-tions per second [24]. To determine the posiopera-tion with radar, detected features of the surroundings can be matched with a pre-built map in the vehicles computer. Another method is to place artificial beacons (such as reflec-tors) at known locations to be detected by radar. The radar system can be disturbed by unexpected objects in the surroundings or bad weather conditions. In both cases, the view of the radar is obstructed resulting in no or faulty detections [24]. To protect the radar from the elements, for outdoor use the sensor is mounted in a protecting cover as can be seen in Figure 4.8.

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Figure 4.8: Radar with protecting cover on top of an automated lifting vehicle [24]

4.2.6 Laser

Laser detection relies on reflected laser beams to obtain a position estimation. It is also known as LIDAR (LIght Detection And Ranging) or LADAR (LAser Detection And Ranging) [36]. As radar, lasers can be used for multiple purposes in an automated vehi-cle. In combination with artificial landmarks it is a good solution for absolute position estimations, but it can be used for collision prevention as well [24].

Although lasers give a high accuracy, it is a costly method if used for position estima-tion. The entire workspace of the vehicle needs to be equipped with enough clearly visible reflectors for the laser sensor to see at least two, but ideally three of them at a time to calculate the vehicles exact position. With two reflectors detected, distances alone are not enough and angles to the reflectors are needed to calculate the position. With three reflectors detected, only distances can determine the vehicles position [28]. This is made clear in Figure 4.9, where the green points are reflectors and red points are estimates of the vehicles position. All the reflectors have known coordinates in a pre-built map stored in the vehicles system. Therefore the vehicles system should be capable of solving some difficult equations. Due to the difficulty of the map matching, laser guidance provides position estimates at a low rate [43]. The laser beam is nar-row, so a small bump in the road could result in not detecting a reflector. Obstacles or weather conditions (such as fog) can limit the view of a laser, also causing the vehicle to miss reflectors. Therefore, and because of the low rate of position estimates, laser guidance only is not suitable for vehicle navigation. In combination with a relative position method, it can be a highly accurate system [43].

Uncertainty in position estimation of autonomous transport systems

Specialization: Transport Engineering and Logistics

Report number: 2012.TEL.7738

Title:

Uncertainty in position estimation

of autonomous transport systems

Author:

B.B. de Keyzer

Uncertainty in position estimation of autonomous

transport systems

Bj¨orn de Keyzer

Transport Engineering and Logistics,

TU Delft

Contents

Chapter 1

Introduction

Chapter 2

Autonomous transport systems

2.1

Indoor systems

2.1.1

Conveyor systems

2.1.2

Automated guided vehicles

2.1.3

Automated storage and retrieval systems

2.2

Outdoor systems

2.2.1

Automated guided vehicles

2.2.2

Automated lifting vehicles

2.2.3

Automated stacking cranes

2.2.4

Ship-to-shore cranes

2.3

Similarities and differences between indoor and

out-door systems

2.4

Summary

Chapter 3

Uncertainty in position

estimation

3.1

Actuator noise and uncertainty

3.1.1

Wheel slip

3.1.2

Side drift

3.1.3

Substantial load variations

3.1.4

Tire wear

3.2

System disturbances

3.2.1

Weather influences

3.2.2

Uneven road surface

3.2.3

Collisions

3.3

Sensor noise and uncertainty

3.3.1

Weather influences

3.3.2

Signal blockage

3.4

Communication

3.4.1

Radio communication

3.4.2

Infrared communication

3.4.3

Guide wire data communication

3.4.4

Inductive loops communication

3.5

Quality of the data fusion