Abstract
Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non trivial optimization problem. In this paper a multi-objective genetic algorithm is proposed to address this problem. Multiple criteria are optimized up to five simultaneous objectives. Simulations results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.
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References
Bäck, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: Comments on the history and current state. IEEE Trans. on Evolutionary Computation 1, 3–17 (1997)
Chen, M., Zalzala, A.M.S.: A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration. Journal Robotic Systems 14, 529–544 (1997)
Davidor, Y.: Genetic Algorithms and Robotics, a Heuristic Strategy for Optimization. Series in Robotics and Automated Systems, vol. 1. World Scientific Publishing Co. Pte. Ltd., Singapore (1991)
Kubota, N., Arakawa, T., Fukuda, T.: Trajectory generation for redundant manipulator using virus evolutionary genetic algorithm. In: IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico, pp. 205–210 (1997)
Rana, A., Zalzala, A.: An evolutionary planner for near time-optimal collision-free motion of multi-arm robotic manipulators. In: UKACC International Conference on Control, vol. 1, pp. 29–35 (1996)
Wang, Q., Zalzala, A.M.S.: Genetic control of near time-optimal motion for an industrial robot arm. In: IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota, pp. 2592–2597 (1996)
Pires, E.S., Machado, J.T.: Trajectory optimization for redundant robots using genetic algorithms. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, USA, p. 967. Morgan Kaufmann, San Francisco (2000)
Pires, E.J.S., Machado, J.A.T.: A GA perspective of the energy requirements for manipulators maneuvering in a workspace with obstacles. In: Proc. of the 2000 Congress on Evolutionary Computation, Piscataway, NJ, pp. 1110–1116. IEEE Service Center, Los Alamitos (2000)
Chocron, O., Bidaud, P.: Evolutionary algorithms in kinematic design of robotic system. In: IEEE/RSJ International Conference on Intelligent Robotics and Systems, Grenoble, France, pp. 279–286 (1997)
Kim, J.O., Khosla, P.K.: A multi-population genetic algorithm and its application to design of manipulators. In: IEEE/RSJ Int. Conf. on Intelligent Robotics and Systems, Raleight, North Caroline, pp. 279–286 (1992)
Han, J., Chung, W.K., Youm, Y., Kim, S.H.: Task based design of modular robotic manipulator using efficient genetic algorithm. In: IEEE Int. Conf. on Robotics and Automation, Albuquerque, New Mexico, pp. 507–512 (1997)
Gacôgne, L.: Multiple objective optimization of fuzzy rules for obstacles avoiding by an evolution algorithm with adaptative operators. In: Proceedings of the Fifth International Mendel Conference on Soft Computing (Mendel 1999), Brno, Czech Republic, pp. 236–242 (1999)
Goldberg, D.E.: GeneticAlgorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation Journal 3, 1–16 (1995)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization (2001)
Horn, J., Nafploitis, N., Goldberg, D.: A niched pareto genetic algorithm for multi-objective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)
Coello, C., Carlos, A.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems 1, 269–308 (1999)
Silva, F., Tenreiro Machado, J.: Energy analysis during biped walking. In: Proc. IEEE Int. Conf. Robotics and Automation, Detroit, Michigan, pp. 59–64 (1999)
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Pires, E.J.S., Machado, J.A.T., de Moura Oliveira, P.B. (2004). Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_64
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DOI: https://doi.org/10.1007/978-3-540-24854-5_64
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