Rapid generation of motion plans for modular robotic systems

Robots are needed to perform important tasks in field environments. A limitation to the practical use of robot systems is their high cost and long development times. It would be desirable to rapidly design and fabricate the hardware and software for these systems, on a time scale of days or weeks instead of years. An integral part of this system is a rapid generator of the motion plans that the robot will execute in order to perform the task. The goal of this research is to develop a methodology for generating these motion plans rapidly, and with as little user intervention as possible. This thesis describes one approach to rapid generation of motion plans for robotic systems operating in field environments. The approach assembles software modules which control robot actions into executable "scripts". For each application, an inventory of modules is provided based on task characteristics. A script's performance is evaluated using off-line simulation of the application robot and environment. Successful scripts for a specific task are found using the genetic algorithm search technique. The thesis discusses the implementation of this approach. The approach is applied to an example restoration task aboard the USS Constitution. The results of this application demonstrate that the scripts can be trained to succeed over the range of characteristic field environment variability. Also, one action module inventory may generate successful scripts for various robot configurations. Furthermore, successful scripts can be generated more quickly by including action modules which solve taskspecific problems. Supervisor: Dr. Steven Dubowsky Title: Professor of Mechanical Engineering

[1]  Henry W. Stone,et al.  HAZBOT: a hazardous materials emergency response mobile robot , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[2]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[3]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[4]  Kenneth E. Kinnear,et al.  Generality and Difficulty in Genetic Programming: Evolving a Sort , 1993, ICGA.

[5]  S. Zein-Sabatto,et al.  Fuzzy controller design using genetic algorithms , 1997, Proceedings IEEE SOUTHEASTCON '97. 'Engineering the New Century'.

[6]  Alexis P. Wieland,et al.  Evolving Controls for Unstable Systems , 1991 .

[7]  M. J. Hicks,et al.  Recursive adaptive filter design using an adaptive genetic algorithm , 1982, ICASSP.

[8]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Werner Neubauer.  Locomotion with articulated legs in pipes or ducts , 1993, Robotics Auton. Syst..

[11]  Akhil J. Madhani.  Design and motion planning of robotic systems subject to force and friction constraints , 1991 .

[12]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[13]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[14]  Simon Handley,et al.  The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions , 1994 .

[15]  Kenneth A. De Jong,et al.  On Using Genetic Algorithms to Search Program Spaces , 1987, ICGA.

[16]  Junichi Akizono,et al.  Field Test of Aquatic Walking Robot for Underwater Inspection , 1990 .

[17]  Graham F. Spencer,et al.  Automatic Generation of Programs for Crawling and Walking , 1993, International Conference on Genetic Algorithms.

[18]  M. G. Cooper,et al.  Genetic design of fuzzy controllers: the cart and jointed-pole problem , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[19]  Nathaniel Rutman,et al.  Automated design of modular field robots , 1995 .

[20]  David E. Goldberg,et al.  ENGINEERING OPTIMIZATION VIA GENETIC ALGORITHM, IN WILL , 1986 .

[21]  Rodney A. Brooks,et al.  A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network , 1989, Neural Computation.

[22]  James S. Tulenko,et al.  10. INTELLIGENT ROBOTICS AND REMOTE SYSTEMS FOR THE NUCLEAR INDUSTRY , 1989 .

[23]  Craig W. Reynolds.  Evolution of obstacle avoidance behavior: using noise to promote robust solutions , 1994 .

[24]  Steven Dubowsky,et al.  Motion planning of mobile multi-limb robotic systems subject to force and friction constraints , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.