Abstract
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.
Similar content being viewed by others
References
Aguirre, E., González, A.: A fuzzy perceptual model for ultrasound sensors applied to intelligent navigation of mobile robots. Appl. Intell. 19(3), 171–187 (2003)
Aha, D.A. (ed.): Lazy Learning. Kluwer, Boston (1997)
Arkin, R.C.: Navigational path planning for a vision-based mobile robot. Robotica 7, 49–63 (1989)
Ashlock, D.A., Manikas, T.W., Ashenayi, K.: Evolving a diverse collection of robot path planning problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1837–1844. Vancouver, Canada, 16–21 Jul 2006
Autonomous mobile robotics toolbox. Dept. Control, Measurement and Instrumentation, Brno University of Technology, Czech Republic. Http://wes.feec.vutbr.cz/UAMT/robotics/simulations/amrt. Cited 21 July 2006 (2006)
Buyurgan, N., et al.: Real-time routing selection for automated guided vehicles in a flexible manufacturing system. J. Manuf. Tech. Manage. 18(2), 169–181 (2007)
Davidor, Y.: Genetic algorithms and robotics: a heuristic strategy for optimization. In: Robotics and Automated Systems, vol. 1. World Scientific, Singapore, 1991
Dijkstra, E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269 – 271 (1959)
Elshamli, A., Abdullah, H.A., Areibi, S. 2004. Genetic algorithm for dynamic path planning. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, pp. 677–680. Niagara Fall, Ontario, Canada, 2–5 May 2004
Ghaffari, M., et al.: Design of an unmanned ground vehicle, bearcat III, theory and practice. J. Robot. Syst. 21(9), 471–480 (2004)
Hagras, H., Callaghan, V., Colley, M.: Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online fuzzy-genetic system. Fuzzy Sets Syst. 141(1), 107–160 (2004)
Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum-cost path. IEEE Trans. Syst. Sci. Cybern. SSC-4(2), 100–107 (1968)
Kumon, E.M., et al.: Autopilot system for kiteplane. IEEE Trans. Mechatronics 11(5), 615–624 (2006)
Latombe, J.C.: Robot Motion Planning. Kluwer, Boston (1991)
Leon, J.A.F., Tosini, M., Acosta, G.G.: Evolutionary reactive behavior for mobile robot navigation. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 532–537. Singapore, December 2004
Li, W., Christensen, H.I., Oreback, A., Chen, D.: An architecture for indoor navigation. In: the 2004 IEEE Conference on Robotics and Automation, vol. 2, pp. 1783–1788. New Orleans, USA, April 2004
Lin, H., Xiao, J., Michalewicz, Z.: Evolutionary navigator for a mobile robot. In: the 1994 IEEE Conference on Robotics and Automation, vol. 3, pp. 2199–2204. San Diego, USA, May 1994
Liu, J., Hu, H., Gu, D.: A layered control architecture for autonomous robotic fish. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 312–317. Beijing, China, 9–13 October 2006
Lozano-Perez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM. 22(10), 560–570 (1979)
Maalouf, E., Saad, M., Saliah, H.: A higher level path tracking controller for a four-wheel differentially steered mobile robot. Robot. Auton. Syst. 54(1), 23 – 33 (2005)
Mahfoud, S. W.: Niching Methods for Genetic Algorithms, Ph. D. Thesis, University of Illinois at Urbana-Champaign (1995)
Malhotra, R., Sarkar, A.: Development of a fuzzy logic based mobile robot for dynamic obstacle avoidance and goal acquisition in an unstructured environment. In: Proceedings of the International Conference on Advanced Intelligent Mechatronics, pp. 1198–1203. Monterey, California, USA, 24–28 July 2005
Mali, A.D.: On the behavior-based architectures of autonomous agents. IEEE Trans. Syst. Man. Cybern. C. 32(3), 231–242 (2002) August
Martinez-Alfaro, H., Gomez-Garcia, S.: Mobile robot path planning and tracking using simulated annealing and fuzzy logic control. Expert Syst. 15(3–4), 421–429 (1998)
Matlab. http://www.mathworks.com. Cited 21 July 2006 (2006)
Mulvaney, D.J., et al.: Real-time machine learning in embedded software and hardware platforms. In: Workshop on Automatic Learning and Real-Time, pp. 65–78. Siegen, Germany, September 2005
Muñoz-Salinas, R., et al.: A multi-agent system architecture for mobile robot navigation based on fuzzy and visual behavior. Robotica 23(6), 689–699 (2005)
Murphy, R.R.: Introduction to AI Robotics. MIT Press, Cambridge, MA, USA (2000)
Na, Y.K., Oh, S.Y.: Hybrid control for autonomous mobile robot navigation using neural network based behavior modules and environment classification. Auton. Robot. 15(2), 193–206 (2003)
Nearchou, A.C.: Path planning of a mobile robot using genetic heuristics. Robotica 16, 575–588 (1998)
Nefti, S., et al.: Intelligent adaptive mobile robot navigation. J. Intell. Robot. Syst. 30(4), 311–329 (2001)
O’Dunlaing, C., Yap, C.K.: A retraction method for planning the motion of a disc. J. Algorithms 6, 104–111 (1982)
Parasuraman, R., et al.: A flexible delegation-type interface enhances system performance in human supervision of multiple robots: empirical studies with RoboFlag. IEEE Trans. Syst. Man. Cybern. A. 35(4), 481–493 (2005)
Patnaik, S., Karibasappa, K.: Motion planning of an intelligent robot using GA motivated temporal associative memory. Appl. Artif. Intell. 19(5), 515–534 (2005)
Payton, D.W., Rosenblatt, J.K., Keirsey, D.M.: Grid-based mapping for autonomous mobile robot. Robot. Auton. Syst. 11(1), 13–21 (1993)
Quinlan, J.R.: Learning efficient classification procedures and their application to chess endgames. In: Michalski, R.S., Carbonell, J., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, vol vol. 1, p. 463. –. 482. Tioga Press, Palo Alto, CA, USA (1983)
Rajapakse, A., Furuta, K., Kondo, S.: Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution. IEEE Trans. Fuzzy Syst. 10(3), 309–321 (2002)
Santos, V.M., Castro, J.P., Ribeiro, M.I.: Nested-loop architecture for mobile robot navigation. Int. J. Robot. Res. 19(12), 1218–1235 (2000)
Sareni, B., Krahenbuhl, L.: Fitness sharing and niching methods revisited. IEEE Trans. Evol. Comput. 2(3), 97–106 (1998)
Shah-Hamzei, G.H., Mulvaney, D.J..: Intelligent process control using fuzzy ITI. Neural Comput. Appl. 9(1), 12–18 (2000)
Sillitoe, I.P.W., et al.: Experiments in robust bistatic sonar object classification for local environment mapping. In: Proceedings of the 2001 IEEE Conference on Robotics and Automation, vol. 2, pp. 2147–2152. IEEE, Seoul, Korea, May 2001
Swere, E. Mulvaney, D.J., Sillitoe, I.P.W.: Efficient incremental decision tree generation for embedded applications. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 2, pp. 1100–1105. IEEE, Singapore, December 2004
Trojanowski, K., Michalewicz, Z., Xiao, J.: Adding memory to the evolutionary planner/navigator. In: IEEE Conference on Evolutionary Computation, pp. 483–487. Indianapolis, USA, April 1997
Utgoff, P.E.: ID5: an incremental ID3. In: the 5th International Conference on Machine Learning, pp. 107–120. San Francisco, USA, 1988
Utgoff, P.E.: Improved training via incremental learning. In: the 6th International Workshop on Machine Learning, pp. 362–365. Morgan Kaufmann, 1989
Utgoff, P.E., Berkman, N.C., Clouse, J.A.: Decision tree induction based on efficient tree restructuring. Mach. Learn. 29, 5 – 44 (1997)
Vaughan, R.T., et al.: Lost: localization-space trails for robot teams. IEEE Trans. Robot. Auto. 18(5), 796–812 (2002)
Wang, Y., Mulvaney D.J., Sillitoe, I.P.W.: Genetic-based mobile robot path planning using vertex heuristics. In: Proceedings of the 2006 IEEE International Conferences on Cybernetics and Intelligent Systems, pp. 463–468. IEEE, Bangkok, Thailand, June 2006
Xiao, J., et al.: Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput. 1, 18–28 (1997)
Xiao, J., Michalewicz, Z., Zhang, L.: Evolutionary planner/navigator: operator performance and self-tuning. In: IEEE Conference on Evolutionary Computation, pp. 366–371. Nagoya, Japan, May 1996
Yang, X., Moallem, M., Patel, R.V.: A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation. IEEE Trans. Syst. Man. Cybern. B. 35(6), 1214–1224 (2005)
Zalama, E., et al.: Adaptive behavior navigation of a mobile robot. IEEE Trans. Syst. Man. Cybern. A. 32(1), 160–169 (2002)
Zheng, C., et al.: Evolutionary route planner for unmanned air vehicles. IEEE Trans. Robot. 21(4), 609–620 (2005)
Zheng, C., Ding, M., Zhou, C.: Real-time route planning for unmanned air vehicle with an evolutionary algorithm. Int. J. Pattern. Recogn. Artif. Intell. 17(1), 63–81 (2003)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, Y., Mulvaney, D., Sillitoe, I. et al. Robot Navigation by Waypoints. J Intell Robot Syst 52, 175–207 (2008). https://doi.org/10.1007/s10846-008-9209-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-008-9209-6