Homing Guidance Schemes for Autonomous Vehicles

Copyrigth © IFAC Motion Control for Intelligent Automation Perugia. Italy. October 27 -29. 19<)2

HOMING GUIDANCE SCHEMES FOR AUTONOMOUS VEHICLES ROTH H. and

K. SCHILLING

FH Ravcnsburg-Wcingaflcn Poslfach 1261. D 7987 Wcingancn. Germany

The aim of this paper is to study motion control schemes on the basis of fuzzy logic for an autonomous mobile robot, equipped with inexpensive optical range sensors, for traversing an only partly known domain. The task is to plan a collision-free path from any starting point to the given target in a terrain with obstacles. The homing guidance task is subdivided into: - the longterm objective of reaching the target with minimum effort, - the short range activities to avoid collisions with other objects and - to perform the final docking to the target (e.g. a workbench). The paper discusses the architecture to link the different control levels. The implemented algorithm is analysed and adapted in computer simulations with respect to efficiency and robustness criteria. There results a robust performance providing a limited precision of the motion. Key words: Mobile Robots, Fuzzy Control, Adaptive Control, Docking Control

1.

INTRODUCTION

A key component in the logistics of industrial production lines are the means to perform the flow of materials, like belt conveyors or transport vehicles. In case of changing routes and material transport frequencies guided vehicles are most suitable. Here with progressing automation human drivers are replaced by Autonomous Guided Vehicles (AGVs).

the systems adaption capabilities. It is of benefit for modem, flexible production cells, providing the means for quick changes in the manufacturing of different product variants. The aim of this paper is to study control strategies, supporting the global path planning by local methods for collision avoidance and target docking. These local strategies are based on relative distance and velocity measurements to the other objects. In the following section the system architecture of the control and simulation environment is scetched. Section 3 provides further details of the fuzzy control algorithms.

In the first generation these AG Vs were based on inductive wires placed in the floor, providing the reference path, and appropriate control systems to correct measured deviations. A major disadvantage are the static pathes determined by the fixed wiring, causing high costs and delays if routes have to be changed.

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Currently a second generation of AGV's is under development, providing an increased flexibility. Here the information on the path is stored in the vehicle's board computer, instead of in the wires laid into the floor. The technology and cost development of modem computer capacities enable a reconfiguration of the vehicles characteristic parameter via software, providing a broad band of adaption capabilities. In particular these virtual pathes stored in a knowledge base on-board improve

In a first step the simulation system was implemented on a workstation to sel ve as an idealized testbed for the development of the control strategies (cf. figure 1). In the compmer simulation all crucial parameters are directly accessible and can be tuned in order to create a particular test situation. Thus it provides an appropriate environment for the development and comparison of the control algorithms without having to deal with any

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Figure I : System architecture of the simulation program disturbing side effects. Nevertheless the final test regarding capabilities and suitability for the practical use must be performed on hardware in an industrial environment. It is currently investigated as next step of the project. The simulation environment is build In a modular way containing the 3 main blocks - user interface, - vehicle and environment simulation, - control algorithms. One objective is to enable later the transfer of the control block without major modification to the vehicle. The user interface provides the facilities to easily interact with the simulation by - definition of the actual parameter at the begin of a simulation, - interactive adaption of parameter during the simulation, - displaying the movement of the vehicle in the given terrain, - monitoring the status of the active control strategy.

The vehicle and environment simulation covers the dynamic properties of the vehicle, the sensor perception of the environment and actual properties of the working environment. There are three different classes of information characterizing the environment : - static properties (like walls, surface slopes etc.), - varying properties (like open/closed doors, storage areas containing more or less boxes, chairs displaced in a certain area, and unforeseen obstacles), - moving objects (like human workers, other vehicles etc.). Courses with obstacles of varying complexity (static/dynamic) are used to assess the performance of the algorithm. The modelling of interactions between the environment and the vehicle is the task of the vehicle simulation. To provide a realistic input to the control algorithms, the sensor signal characteristics and processing have to be simulated. Of importance are the spatial distribution of beams, interactions between different sensors, the material dependant reflection characteristics and the obtainable resolution. At the current state ultrasonic and

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infrared range sensors are addressed, but also gyros and cameras will be implemented .

3.Control Strategie-; - Details and Perfonnance Comparisons

The effects of the control are derived from the vehicle dynamics model. Here currently a three wheel robot vehicle is implemented, but due to the modular architecture also alternative AGV-models can be introduced. The actual dynamics depends on vehicle characteristics (like friction coefficients of the wheels, admissible range of steering angle, etc.), the payload weight/dynamics (liquids/solids effecting the actual center of gravity), and the terrain properties (like surface roughness, inclines etc.). Available control actions affecting the dynamics are the robot's speed and steering angle. Further details regarding the control are given in the following section.

The control segments task is to provide the motion control schemes for an autonomous mobile robot traversing an only partly known domain to reach a target working place. It hosts some standard algorithms for global navigation as well as the methods for collision avoidance and docking.

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Table 1 : Components of the homing guidance scheme Component Initiation Criterium Priority path generation all other components 3 not active collision avoidance object in the "near" range of the distance 1 sensors docking acquisition of target 2 beacon by the sensors

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The collWon avoidance requires quick reactions on obstacle detections (by example via ultrasonic or infrared range sensores, camera pictures). As long as activated by the range sensors this mode receives always first priority. A secondary objective is the energy efficiency of the detour manoeuvre. For path generation a linear interpolation between actual position and the target is employed. A certain deviation in the position measurement is tolerated, because in our application it is not important to follow accurately a certain path during the transfer. It only has to be assured, that the target beacon range is entered. Then the docking component is responsible for th~ succeeding control manoeuvres. At docking to the target workbench, the vehicle has to reach the specified position and orientation for succeeding manipulations (most frequently loading and unloading). Here a strategy, providing higher accuracies the clother the vehicle approaches the target, is required. In both cases the control algurithms depend very much on the quality of sensor measurements relative to the object.

The fuzzy inference containes the rules, detenning the reaction in dependence on the different combinations of input classes. Figure 3 presents as example some part of the rules for collision avoidance. In addition to the three forward pointing range sensor beams (as displayed in figure 2) also the variable ' relative orientation' is used as input, describing the angle between the vehicle axis and the direction from the current AGV location towards the target. Thus global path efficiency aspects are introduced into the local collision avoidance scheme. The more input variables and classes the more rules result for responses to the input combinations. Although not all combinations must actually be correlated to an output, the response time will be deteriorated due to the extended search space. As result of the inference, the input measurements are correlated to the fuzzy sets of the output classes. The derivation of the control value from the memberships to fuzzy sets is task of the defuzzification procedure. In our experiments the center-of-maximum approach turned out to be the quickest of available alternative methods.

Fuzzy Logic provides the means to transfer a linguistic model of a process control (as used by human operators) to mathematical treatement (cf. [4),[2] for the general background). In fuzzy control the sensor data are processed in the three subsequent steps fuzzification, fuzzy inference and defuzzification to generate the control action .

In case of docking the target environment - exept obstacles - is usually well known. Thus in case of high target position accuracy requirements together with a good model of the environment (geometry, dynamics), adaptive control schemes would be most appropriate.

In the fuzzification step the measurements are related to the appropriate fuzzy set by employing the selected membership functions. The choice of membership function s critically influences the results and must be tuned according to the sensor performance. With respect to time and energy efficiency as well as robustness, the parameter of the membership functions have been optimized according to the simulation results.

If a lower precision is sufficient, fuzzy controls might be preferred, as the processor capacity requirements are lower as well as cheaper, less accurate sensor data can be suitably handled. The developed fuzzy docking concept is based on two steps: - guidance towards the final docking domain (cf. Fig.4), - beacon guided final docking (cf. Fig.5) .

In the middle column of figure 2 the particular membership functions used for collision avoidance are displayed. There are three range classes (close, medium, far) for each of the three forward sensors introduced. If a higher accuracy, on the price of increased reaction time, is required, the amount of fuzzy set classes can be increased. In the domain of high accuracy, the discretisation has to be finer and a ~oncentration of membership functions with small activity areas results. We used always piecewise linear member!.hip functions, as these exhibit best processing speed.

The fuzzy sets during approach (cf.Fig,4) are related to the scetched 10 circle segments in a "far" (between 1.5 and 4 times the vehicle length) and "close" (up to 2 times the vehile length) range to the target. Figure 4 represents the rule set to control the vehicles velocity and steering angle in depence upon position. For better distinction the overlap between the fuzzy sets is neglected in this figure.

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4. Perspectives The emphasis of this paper was to discuss simple control schemes for motion control on the basis of low cost equipment. In particular simple, but efficient fuzzy control algorithms for colIision avoidance and docking have been developed. Compared to other recent approaches to this topic [3] simpler and less expensive optical sensors have been employed as welI as less complex control strategies. Comparing the results of alternative approaches regarding collision avoidance, lead to the following recommendations : If sufficient information is available to characterize the obstacles (dynamics, shape) in mathematical models, the adaptive control algorithm provides the best results. If no environmental model but a lot of examplary data about the obstacles can be provided, these can used to train a neuro-controller, exhibiting good performances as long as the actual environments does not differ too much from the training examples. Further experiments are in progress to generate additional details on the algorithms' performance due to the criteria robustness, real-time performance, safety, energy consumption and time allocation to perform a mission. The discussed methods are to be transferred to a test vehicle to evaluate the performance on real sensor data and vehicle dynamics. The technologies studied in this paper are expected to be of benefit for developments beyond the industrial environment, discussed in this paper. By example robots for private households have to operate under high safety requirements in a more unstru::.tured environment, which is hard to model. Here w.ry challenging control problems are to be approached. While the high requirements are due to the human envimment. this fact also enables to restrict the autonomy capabilities and to include human interaction to create economically reasonable solutions.

planned planetary sample acquisition missions complex operations are to be performed in rather simple, but uncertain environments. This implys the need for challenging on board decision capacities.

Acknowledgements This research was supported as lIT-project by the ministery of culture and research BadenWurttemberg. We thank our collaborators B. Theobold, U .Naab and A. Herrmano for their engaged contributions to the project.

References [1]

Cox,I.J.lWilfong,G.T. Autonomous Robot Vehicles Springer Verlag 1990

[2]

Kosko,B. Neural Networks and Fuzzy Systems Prentice Hall 1992

[3]

Ollero,A.lCamacho,E.F. (eds.) Intelligents Components and Instruments for Control Applications (SICICA '92) Preprints of the IF AC Symposium, Malaga 1992

[4]

PedryCZ,W. Fuzzy Control and Fuzzy Systems John Wiley &Sons 1989

[5]

Schilling,K.rrrogus,W. Autonomy Requirements of Interplanetary Spacecraft IAF-90437, 1990

Much research effort is concentrated on vehicles, able to operate a! emergency cases in dangerous tCiTestrian environment1>. Potential application areas concern hazards related to fire, nuclear plants, etc .. Here complex, hardly predictable working environments raise very hard control problems, which might be approached partly by human interaction via teleoperations. Another extreme for autonomy occurs in space exploration [5], where due to huge signal propagation delays the potential for direct human interaction is very limited. On the other side for the

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