Fuzzy Navigation of Robot System

Transcription

1 Fuzzy Navigation of Robot System DIMITAR LAKOV, STANISLAV VASILEV Department of Intelligent Computer Technologies Institute of Computer and Communication System Bulgarian Academy of Sciences Acad. G. Bonchev street Bl. 2, Sofia 1113 BULGARIA Abstract: - The paper presents fuzzy navigation system for a mobile robot. This navigation is based on fuzzy inference machine that performs path planning and obstacle avoidance in arbitrary complex environments. The system is built as two-level hierarchical system. On the higher level there is a controlling PC that is connected with robot control unit through duplex, wireless communication channel. A sensor system comprising four subsystems: tactile, ultrasonic, odometer, and visual is cared for collection of environment information. The inference machine consists of two mutual connected fuzzy rule bases responsible for path planning and obstacle avoidance. The fuzzy navigation strategy is obtained as a combined compromise decision between them. Key-Words: - mobile robot, fuzzy inference system, fuzzy navigation. 1 Introduction The main challenge of modern techniques for autonomous robot navigation systems in real-world environments is neither paramount tracing nor perfect final positioning in well defined ambient conditions, but assessment, interpretation and decision making in fast variable, intrinsically uncertain environment. A good example supporting this assertion is DARPA (Urban Challenge Autonomous Vehicle Competition) [7]. In its recent editions the winners are not among fastest, strongest, and the most intelligible, but highly adaptable ones able to perform above mentioned three conditions. An important problem in autonomous navigation is the need to cope with large amount of uncertain information that is inherent for natural environment. Fuzzy logic appears to be a promising tool to merge a gap between classical techniques and intelligent soft computing based ones adequately performing these requirements. A well study of classical navigation strategy is given in [1]. Driankov et. all present fuzzy logic applications in the field of intelligent system applications [2]. An indispensable part of intelligent autonomous robot systems is a sensor system rich enough to accept and interpret ambient environment. At the same time it has to be able to assess uncertain information. As a consequence a problem with fusion of different information sources arises. Another problem is behaviour fusion of different control actions that are generated as a result of different information sources. Such problems of real-time control systems are among the most important problems that are considered in [3]. The next problem with high importance for right functioning is collision avoidance in natural environments. Many researches work in this direction. An example of this is the work connected with collision avoidance of autonomous guided vehicle using fuzzy logic navigation control. Pin and Watanabe comment some problems in driving a car by means of reflexive fuzzy behaviours that can be applied equally well in autonomous robot system [5]. A good investigation about fuzzy logic application in autonomous robot navigation is presented by Saffiotti in [6]. A detail survey in all these important fields is out of goals we state in the paper. We try only to mark some of them as initial point of investigation and a necessary incentive for recent work. The aim of this investigation is to present a possible way of fuzzy logic application in the field of autonomous navigation. We focus on two basic navigation strategies and their implementation with classical fuzzy logic. The paper is organized as follows: The second section presents the robot characteristics: some consideration for choosing of mechanical construction, information system, communication facilities, and control structure. The third section deals with intelligent navigation system, where a combined path tracing/obstacle avoidance system is proposed. Both use a simplest possible fuzzy rule base inference mechanism. The fourth section comments a sample experiment, and at the end we present a discussion about system compliance and trends in its future development. ISBN: ISSN:

2 2 The Object The object of investigation is presented in Fig. 1. The prototype has been built over caterpillar tank construction. The reason is to have a reliable, fast enough (up to 2m per second) mobile robot able freely to operate and overcome some obstacles. Fig. 1 The prototype As is shown in Fig.1, it consists of head, platform, and base unit. The head has two degrees of freedom to mimic human neck swivelling and joggling. It carries the most of the sensor systems. These two degrees of freedom performs continuous scanning of working space in left/right and up/down direction for good observing of the environment. The Base unit is set in movement by two independent caterpillar direct current drive units. A combination of velocity and direction of movements of both caterpillars produces controllable smooth rotation over centres placed on cross direction line. Depending on combined velocity and direction the rotation centre is moved from left to right infinities. When it is placed in infinities the robot moves forward/backward directions. If the centre coincides with the geometrical centre of the tank, the last spins at a place in clock/contra wise directions depending on the caterpillars control. All intermediate situations correspond to moving with different circular curves. 2.1 Information Structure An information system comprises the following sensor systems: Tactile. It senses robot path in back and forward directions, detecting probable obstacles. It is used as well as in emergency situations Ultrasonic. The ultrasonic range finder SRF04 provides precise, non-contact distance measurement in the range of its defined cone shape area. The information of the distance is presented as time intervals sent to a main controller. Odometer ADXL202E. Its measurements integrate translations and rotations of the robot, updating by this way robot position and orientation in the space. Two axis tilt sensor can measure both dynamic acceleration (vibration) and static acceleration (gravity). The position of the tracks is also measured by means of two incremental encoders. Both systems are used to provide information not only for the robot position, but also for its declination. Sound system. Built on Winbond s ISD2560. It realises sound communication. Winbond s ISD2560 provide high-quality, single-chip, Record/Playback solutions for 60-seconds messaging. The CMOS device includes on-chip oscillator, microphone preamplifier, automatic gain control, antialiasing filter, smoothing filter, speaker amplifier, and high density multi-level storage array. The system uses thirty pre-recorded worlds expressing different situations. Video. Performed as high resolution CCD camera it supplies video information using two grades of freedom. In this way the control system obtain information about working environment inspecting robot tracing. Its primary function is to track and monitor highly contrasting black/white regions. It can also detect motion, provide statistics, and transmit image information to a computer for additional processing. Wireless connection. It provides data transfer between robot sensing system and controlling PC. It is realized by single chip RF Transceiver RF- NRF401, 433 MHz ISM (Industrial, Scientific and Medical) that assure operating bit rate up to 20kbit/s in duplex mode. 2.2 Hardware The robot control system is two-level hierarchical: i. Low level, responsible for sensor processing, robot movement, obstacle reactions, and wireless data transfer to the high level, ii. High level that performs information interpretation, strategy formation, and navigation. Conditionally we call them: i. Firmware for built in robot microprocessor software implementation, ii. Software - for conventional processing of information, iii. Brainware. Intelligent part of control PC that realises soft computing ideas. 2.3 Firmware Among a few fuzzy oriented processors we chose HCS12 free scale processor family [8]. It supports some instructions that are useful for custom fuzzy logic ISBN: ISSN:

3 programs. They are: defining of membership grades, fuzzy logic rule evaluation, weighted average, etc. As a representative of firmware decisions and imbedded processor a Beckhoff TwinCAT software system is chosen [8]. It turns out to be fully compatible with conventional PC combined with Programmable Logic Controllers PLC. A multi-plc can operate simultaneously with it in on line system programming cycle and run-time systems. TwinCAT offers a precise time-base in which programs are executed with the highest deterministic features, independently of other processor tasks. TwinCAT supports all the IEC programming languages with convenient editors and a fast, effective compiler. The program media offers freely choice between six programming languages. One of them is ST, similar to conventional C that can be adapted to other platforms. In addition it supplies a library to be used for additional control, visualisation and communication. 2.4 Software A software implementation of intelligent control can be performed over standard RISK or Harvard architecture. Depending on application embedded or desktop system that is used an appropriate program tool is required. Most of researches in the field of software tools work with specific program environments that assure good testing and simulating facilities for modelling of fuzzy systems. For example: Fuzzy Logic Toolbox for MATLAB, FuzzyTECH and so. There are also software tools convenient for embedded applications which translate linguistic and logic statements to common source code, executable for C-compiler. Our solution is Fuzz-C TM. Fuzz-C TM desktop computer for building fuzzy logic systems has the following advantages: i. User friendly dialog in fuzzytech media. It offers light implementation of fuzzy logic ideas: definition of in/out as linguistically variables, different type of systems, fuzzification/defuzzification, assessment of rule compliance, etc., ii. Convenient visualisation, simulation and testing facilities, iii. Compilation of source code into C or other convenient languages, iv. Acceptable possibilities to transform the highest level of software construction into executable Brainware. 2.6 Brainware The highest level of the intelligent control system is called conditionally Brainware [9]. It performs two tasks: assessment of a highly variable and ill defined robot environment, and creation of flexible control strategy. This part is localised as a separate block independent from the other system constituents. It is tuned and upgraded in the way similar to human brain abilities of self organization. Although, up to now, its implementation is in the structure of the control PC, in sequel development of the system we envisage it to be placed as an additional block unite in the robot control structure. Such implementation increases the robot reliability. In fact, the last depends at high extent on wireless connection, communication tract throughput, and environment disturbances. Placing it in the robot structure one can gain in the following two aspects: above mentioned reliability, as well as a necessary step in the future performance of fully autonomous robot structures. The next section is dedicated mainly to this kern of the control system that is essence of the recent contribution. 3 Intelligent Navigation System The aim of navigation is to provide the robot control system with timely and up to date information in accordance with predefined goals. As a rule it comprises environment model, vehicle model, and a set of criteria. The navigation tasks are chosen among admissible states of the first two models that fulfil in the best way the set of criteria. In most of the cases a precise description of these models fails to fulfil timely many of these tasks. In this case we make many simplifications of the models, which lead to deterioration of the task. In our investigation we use another approach based on soft computing techniques. It sacrifices precise description at the expense of intuitively involved linguistically variables. Such navigation systems we call intelligent since they reflect in some extent human intuition. Functionally, the intelligent navigation system is shown in Fig. 2. Sensor data transferred from the robot to PC are processed independently and after their fusion are supplied back to the robot. Sensor Data Physical Area1 Physical Area1 Fuzzyfication Fuzzyfication First Level Behavior 1 Inference Behavior 2 Inference Defuzzyfication Defuzzyfication Fig. 2 Robot Navigation System Second Level Integration of different Behaviors ISBN: ISSN:

4 Navigation solution is an admissible compromise between decisions. Every decision is transferred to on board robot control system where it is interpreted and supplies both independently acting DC caterpillar power units. 3.1 Fuzzy Robot Navigation In order to perform both navigation strategies: obstacle avoidance, and path tracing, the control system uses a sectors distribution of working space under control. Conditionally it is divided into six regions. The range of sectors takes into consideration the cone shape area of ultrasonic sensor, and.observing ability of rotating head. In fuzzy interpretation manner there are not sharp boundaries between regions. In Fig. 3 you can see the distribution of accepted regions. left side left front front back side right front right side Fig. 4 Space Position The following abbreviations are accepted: SP: very near, vn; near, nr; and far, fr; VA: slow, sl; fast, fs; AA: left shift, ls; zero shift, zs; right shift, rs; AD: left behind, lb; right side, rs; front, ft; left side, ls; right behind, rb; VT: slow, sl; fast, fs; VT has two additional crisp values: stop, st; and go forward, fw. The three in/two out system obtains complete fuzzy rule base with eighteen rules. Fig. 3.Sector Distribution of Working Space In what follows sectors are interpreted as terms of linguistically variable. All actions are taken bearing in mind the information of this distribution. A combined obstacle avoidance/path tracing strategy is reflected into fuzzy rule base of robot functioning. The ultrasonic system performs two tasks: Defining of obstacles and final goal position, Assessment of their angular deviation in respect to current robot position. Both supply navigation system for next fuzzification and processing. 3.2 Environment representation It is accepted fuzzy environment representation via three in/two out linguistically variables: Inputs: Space Position, SP; Velocity Assessment, VA; Angle Assessment, AA; Outputs: Angular Deviation, AD; Velocity Trace, VT; In fuzzy inference mechanism they take their values in accordance with representations shown in Fig.4 to Fig. 8, respectively: Fig.5 Velocity Assessment Fig. 6 Angle Assessment ISBN: ISSN:

5 Table 3 AD and VT as a Function of SP and AA, VA= sm fr rs/fs fw/fs ls/fs Table 4 AD and VT as a Function of SP and AA, VA= fs Fig. 7 Angular Deviation Fig. 8 Velocity Trace 3.3 Inference System For better visualisation Path Tracing Strategy PTS of fuzzy rule base is represented separately by means of Table 1 and Table 2. The third fuzzy variable VA takes its values separately in Fig. 1 and Fig. 2 for both: sm and fs. Table 1 AD and VT as a Function of SP and AA, VA= sm fr rs/fs fw/fs ls/fs fr rs/fs zs/fs ls/fs Fuzzy inference machine uses classical centre of gravity defuzzification principle. As it can be seen both strategies have identical fuzzy rule base representation. The difference is that PTS is strategically goal oriented, whiles the OAS is tactical. Although every one of them is checked in every scanning cycle the integral decision is taken following three simple rules: Both strategies influence on the angular deviation leaving already accepted velocity of tracing unchanged, In every situation the obstacle avoidance strategy has higher priority than the path tracing, In case of irresolvable conflict the robot stops and emits a sound emergency signal. The first two assure relatively smooth tracing, while the last means that in case of contradictory decisions both strategies are ignored until contradictory situation vanishes. 4 An Experiment In Fig. 9 it can be seen a sample example of the navigation system with three obstacles on 300x400cm working polygon. Table 2 AD and VT as a Function of SP and AA, VA= fs fr rs/fs zs/fs ls/fs The difference between input variables of two tables is the value of third variable VA Analogously Obstacle Avoidance Strategy OAS of fuzzy rule base is represented by Table 3 and Table cm Fig. 9 A navigation among three obstacles oa ISBN: ISSN:

6 Dotted lines represent initial sensing of obstacles and the goal. The objects are sensed in every step supplying by this way navigation system. Due to autonomous acting sensor systems, simple fuzzy rule base representation, and simplified choice of navigation strategy the response control reaction is within 50msec range. This means that control actions is supplied in steps every 0,1sm of robot motion by typically fastest robot velocity 2cm per second. As a consequence a smooth tracing line is obtained. The distance between obstacles is chosen to be greater than the robot width so that there is no emergency situation in accordance with the third rule, which can break the process of tracing. Simple comparison with a classical navigation system shows the following advantages of fuzzy based navigation: i. The classical needs a complex environmental model comprising object identification with multiple on line measurements and sequel control actions. Hence the navigation system is complicated, motion control is delayed and tracing curve is not smooth, ii. Fuzzy navigation system identifies only boundaries of tracing obstacles and goal and makes decisions accordingly. There no need of other model and decisions are made by simplest and fastest way without deteriorating the trajectory to be traced, iii. The fuzzy navigation is open. That means any improvement can be simply done by upgrading of fuzzy rule base instead of new complex model creation as is the case with the classical, iv. Unified fuzzy interpretation enlighten process of information fusion of different information sources without complicated transformations, v. The most attractive feature in comparison with the classical one is a mimicking of human behaviour that has been well proven long time ago. 5 Discussion The paper presents an attempt to model mobile robot behaviour in terms of fuzzy logic. We understand quite well that this experiment can be regarded as an initial step only in the broad field of fuzzy robot modelling. At the introduction we mark five problems that meet such investigation. Now we comment their implementation: 1. Intelligent navigation: a combined path tracing/ obstacle avoidance, 2. Environment sensing: four sensing subsystems that form an abstract image presentation, 3. Information fusion: unitary approach by means of fuzzy rule base interpretation, 4. Integrated decision involving priority of the obstacle avoidance strategy, 5. Computing architecture: two level hierarchical using remote controlling PC. The primary modelling stimulates some system improvements concerning every point: 1. Involvement of other types of navigation strategies that can broaden the application field, 2. Additional sensing subsystems for example thermal. As far as every system is autonomous additional system will not cause any complication, 3. Experiments upon more complex fuzzy rule base representation. The recent reflects only primary reactions of environment stimuli, 4. Refinement of mechanisms for choosing of different navigation strategies. It can improve dynamical characteristics of the control strategies, 5. Realization of imbedded Brainware robot control structure. In this case remote control of PC can be used for distributed behaviour of multi-robot system. Above mentioned suggestions remark our intentions of future development. References [1] R. S. Arkin, Motor Schema-base Mobile Robot Navigation, International Journal of Robotic Research, Vol. 8, [2] D. Driankov, and A. Saffotti, eds. Fuzzy Logic Techniques for Autonomous Vehicle Navigation. Springer-Physica Verlag, DE, [3] Goodridge, et. all, Multilayer Fuzzy Behaviour Fusion for Real-time Control of Systems with many Sensors, in Proc. IEEE Int. Control of Multisensor Fusion and Integration for Intelligent Systems (MFI), Las Vegas, October 1994, pp [4] C. Lee,.and P. Wang, Collision Avoidance by Fuzzy Logic Control for Automated Guided Vehicle Navigation, Journal of Robotic Systems, Vol.11 Aug. pp , [5] F. G., Pin, Y. Watanabe, Driving a Car Using Reflexive Fuzzy Behaviours, in Proc. of the Second International Conference on Fuzzy Systems, San Francisco, USA [6] A. Saffiotti, The uses of Fuzzy Logic in Autonomous Robot Navigation, Center for Applied Autonomous Sensor Systems, Orebro University, Sweden, S [7] iew.asp [8] /ref_manual/s12cpuv2.pdf?fsrch=1 [9] L. A. Zadeh, A New Direction in AI Toward a Computational Theory of Perceptions. AI Magazine 22(1): ISBN: ISSN: