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Model–based energy efficient global path planning for a four–wheeled mobile robot

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper concerns an energy efficient global path planning algorithm for a four-wheeled mobile robot (4WMR). First, the appropriate graph search methods for robot path planning are described. The A* heuristic algorithm is chosen to find an optimal path on a 2D tile-decomposed map. Various criteria of optimization in path planning, like mobility, distance, or energy are reviewed. The adequate terrain representation is introduced. Each cell in the map includes information about ground height and type. Tire-ground interface for every terrain type is characterized by coefficients of friction and rolling resistance. The goal of the elaborated algorithm is to find an energy minimizing route for the given environment, based on the robot dynamics, its motor characteristics, and power supply constraints. The cost is introduced as a function of electrical energy consumption of each motor and other robot devices. A simulation study was performed in order to investigate the power consumption level for diverse terrain. Two 1600 m2 test maps, representing field and urban environments, were decomposed into 20x20 equal-sized square-shaped elements. Several simulation experiments have been carried out to highlight the differences between energy consumption of the classic shortest path approach, where cost function is represented as the path length, and the energy efficient planning method, where cost is related to electrical energy consumed during robot motion.
Rocznik
Strony
337--363
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
  • Industrial Research Institute for Automation and Measurements PIAP, Al. Jerozolimskie 202, 02-486 Warsaw, Poland
autor
  • Industrial Research Institute for Automation and Measurements PIAP, Al. Jerozolimskie 202, 02-486 Warsaw, Poland
Bibliografia
  • 1. Bigaj, P. (2012) Global Path Planning for Mobile Robots. Oficyna wydawnicza PIAP, Warszawa. Borgstrom, P., Singh, A., Jordan, B., Sukhatme, G., Batalin, M., Kaiser, W. (2008) Energy based path planning for a novel cabled robotic system. IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE , 1745-1751.
  • 2. De Filippis, L., Guglieri, G., Quagliotti, F. (2011) A Minimum Risk Approach for Path Planning of UAVs. Journal of Intelligent & Robotic Systems, 61(1-4), 203-219. Dogan, A.(2003) Probabilistic approach in path planning for UAVs. 2003 IEEE International Symposium on Intelligent Control. IEEE, 608-613.
  • 3. Dolgov, D. T., Thrun, S., Montemerlo, M., Diebel, J. (2008) Path Planning for Autonomous Driving in Unknown Environments. Proceedings of the Eleventh International Symposium on Experimental Robotics (ISER-08). Springer, Berlin–Heidelberg.
  • 4. Greytak, M., Hover, F. (2009) Motion Planning with an Analytic Risk Cost for Holonomic Vehicles. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai. IEEE, 5655- 5660.
  • 5. Heart, P. E., Nilsson, N. J., Bertram, R. (1968) A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107.
  • 6. Hendzel, Z. (2007) An adaptive critic neural network for motion control of a wheeled mobile robot. Nonlinear Dynamics, 50 (4), 849–855.
  • 7. Hong Jun, K., Byung Kook, K. (2010) Minimum-energy trajectory planning on a tangent for battery-powered three-wheeled omni-directional mobile robots. Control Automation and Systems (ICCAS), 2010 International Conference, Gyeonggi-do. IEEE, 1701-1706.
  • 8. Katoh, R., Ichiyama, O., Tamamoto, T., Ohkawa, F. (1994) A real-time path planning of space manipulator saving consumed energy. Control and Instrumentation, 1994. IECON ’94, 20th International Conference on Industrial Electronics. IEEE, 1064-1067.
  • 9. Lau, B., Sprunk, C., Burgard, W. (2009) Kinodynamic motion planning for mobile robots using splines. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, St. Louis. IEEE, 2427-2433.
  • 10. Liu, S., Sun, D. (2011) Optimal Motion Planning of a Mobile Robot with Minimum Energy Consumption. 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Budapest. IEEE, 43-48.
  • 11. McNinch, L. C., Muske, K. R., Ashrafiuon, H., Peyton, J. C., Soltan, R. A. (2011) Real-time Coordinated Trajectory Planning and Obstacle Avoidance for Mobile Robots. Journal of Automation, Mobile Robotics & Intelligent Systems, 5(1), 23-29.
  • 12. Mei, Y., Lu, Y.-H., Hu, C. Y., Lee, G. (2004) Energy-Efficient Motion Planning for Mobile Robots. Proceedings of the 2004 IEEE International Conference on Robotics 8 Automation, New Orleans. IEEE, 4344 4349.
  • 13. Mei, Y., Lu, Y.-H., Lee, G. C., Hu, Y. C. (2006) Energy-Efficient Mobile Robot Exploration. Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando. IEEE, 505-511.
  • 14. Moosavian, S., Alipour, K., Bahramzadeh, Y. (2007) Dynamics modeling and tip-over stability of suspended wheeled mobile robots with multiple arms. Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference, San Diego. IEEE, 1210-1215.
  • 15. Park, S., Minor, M. (2004) Modeling and dynamic control of compliant framed wheeled modular mobile robots. Proceedings. ICRA ’04. 2004 IEEE International Conference, 4. IEEE, 3937 - 3943.
  • 16. Pousti, A., Bodur, M. (2008) Kinematics and Dynamics of a Wheeled Mobile Inverted Pendulum. 2008 International Conference on Computational Intelligence for Modelling Control & Automation, Vienna. IEEE, 409-413.
  • 17. Russell, S., Norvig, P. (1995) Artificial Intelligence - A Modern Approach (3rd ed.). Pearson Education, New Jersey.
  • 18. Stentz, A. (1995) The focussed D* algorithm for real time replanning. Pro- ceedings of the International Joint Conference of Artificial Intelligence. San Francisco. Morgan Kaufman Publishers, 1652-1659.
  • 19. Stentz, A., Ferguson, D. (2005) An interpolation-based path planner and replanner. Proceedings of the Int. Symp. on Robotics Research (ISRR).Springer, Berlin–Heidelberg, 1-10.
  • 20. Szulczyński, P., Pazderski, D., Koz lowski, K. (2011) Real-Time Obstacle Avoidance Using Harmonic Potential Functions. Journal of Automation, Mobile Robotics & Intelligent Systems, 5(3), 59-66.
  • 21. Velazquez, R., Lay-Ekuakille, A. (2011) A review of models and structures for wheeled mobile robots: Four case studies. Advanced Robotics (ICAR), 2011 15th International Conference, Tallinn. IEEE, 524-529.
  • 22. Winter, S. (2002) Modeling Costs of Turns in Route Planning. GeoInformatica, 6(4), 345-361.
  • 23. Wong, J. Y. (2001) Theory of Ground Vehicles (3rd ed.). Wiley-Interscience.
  • 24. Yang, G., Zhang, R. (2009) Path Planning of AUV in Turbulent Ocean Environments Used Adapted Inertiaweight PSO. Fifth International Conference on Natural Computation. IEEE, 299-302.
  • 25. Yu, Y., Wang, H., Yuan, Q. (2011) Application of Dijkstra algorithm in robot path-planning. Second International Conference on Mechanic Automation and Control Engineering (MACE). IEEE, 1067-1069.
  • 26. Zheng, S., Reif, J. (2003) On Energy-minimizing Paths on Terrains for a Mobile Robot. ICRA ’03. IEEE International Conference on Robotics and Automation, 3. IEEE, 3782-3788.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6b8e7a04-f3df-4876-a6c9-96691d5ca2d7
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