Abstract
This paper addresses a point-to-point robotic arm path planning in complex obstacle environments. To guarantee a smoothness of a motion during a manipulation, a continuous function of a sixth degree polynomial is utilized as a joint angle path. The feasible sixth degree joint angle path will be searched utilizing a Particle Swarm Optimization (PSO). There is no information regarding the region of this feasible joint angle so that the PSO should search it first. At the first computation where the population is generated randomly, all particles commonly collide with obstacles. The searching computation will be continued till at certain iteration for which the feasible particle is met. Then, the PSO should evolve this particle to find the best one with the highest fitness value. It is very hard computation since it involves a requirement to escape from zero fitness. The most difficult computation in this case is in finding at least one particle that lies in the feasible zone. In this paper, the PSO has shown its good performance in finding the feasible motion of the sixth degree polynomial joint angle path by utilizing just the information of a forward kinematics. To simulate the proposed path planning, 3-Degree of Freedom (DOF) planar robot will be utilized.
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References
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1948)
Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part I: background and development. Natural Computing Journal 6, 467–468 (2007)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73
Chettibi, T., Lehtihet, H.E., Haddad, M., Hanchi, S.: Minimum Cost Trajectory Planning for Industrial Robots. European Journal of Mechanics 23, 703–715 (2004)
Chettibi, T.: Synthesis of Dynamic Motions for Robotic Manipulators with Geometric Path Constraints. Mechatronics 16, 547–563 (2006)
Boriga, M., Grabos, A.: Planning of Manipulator motion Trajectory with higher-degree Polynomials use. Mechanism and Machine Theory 44, 1400–1419 (2009)
Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mechanism and Machine Theory 42, 455–471 (2007)
Gasparetto, A., Zanotto, V.: Optimal trajectory planning for industrial robots. Advances in Engineering Software 41, 548–556 (2010)
Pires, E.J.S., Oliveira, P.B.M., Machado, J.A.T.: Manipulator Trajectory Planning using a MOEA. Journal of Applied Soft Computing 7, 659–677 (2007)
Saravanan, R., Ramabalan, S., Balmurugen, C.: Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles. Engineering Applications of Artificial Intelligence Journal 22, 329–342 (2009)
Pires, E.J.S., Machado, J.A.T., de Moura Oliveira, P.B.: Robot Trajectory Planning using Multi-Objective Genetic Algorithm Optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 615–626. Springer, Heidelberg (2004)
Garg, D.P., Kumar, M.: Optimization Techniques Applied to Multiple Manipulators for Path Planning and Torque Minimization. J. Eng. App. of Artificial Intell. 15, 241–251 (2002)
Yang, X., Wang, H., Zhang, C., Chen, K.: A method for mapping the boundaries of collision-free reachable workspaces. Mechanism and Machine Theory Journal 45, 1024–1033 (2010)
Conkur, E.S.: Path planning using potential fields for highly redundant manipulators. Robotics and Autonomous Systems 52, 209–228 (2005)
Cheng, H., Cheng, H.D.: Feasible map algorithm for path planning. Robotics and Autonomous Systems 17, 257–268 (1996)
Bazaz, S.A., Tondu, B.: Minimum Time On-line Joint Trajectory Generator Based on Low Order Spline Method for Industrial Manipulators. Robotics and Autonomous Systems Journal 29, 209–228 (1999)
Chong, J.W.S., Ong, S.K., Nee, A.Y.C., Youmi, K.Y.: Robot Programming using Augmented Reality: An Interactive Method for Planning Collision-free Paths. Robotics and Computer-Integrated Manufacturing Journal 25, 689–701 (2000)
Rodriguez, A.G.G.: Collision-free Motion Planning and Scheduling. Robotics and Computer-Integrated Manufacturing Journal (2011) (article in press, corrected proof)
Yahya, S., Moghavvemi, M., Mohamed, H.A.F.: Geometrical approach of planar hyper-redundant manipulators: Inverse kinematics, path planning and workspace. Simulation Modelling Practice and Theory 19, 406–422 (2011)
Pereira, G.S., Kumar, V., Campos, M.F.M.: Closed Loop Motion Planning of Cooperating Mobile Robots using Graph Connectivity. Robotics and Autonomous Systems 56, 373–384 (2008)
Clark, C.M.: Probabilistic Road Map Sampling Strategies for Multi-robot Motion Planning. Robotics and Autonomous Systems 53, 244–264 (2005)
Marcos, M.G., Machado, J.A.T., Perdicou´lis, T.P.A.: A Fractional Approach for the Motion Planning of Redundant and Hyper-Redundant Manipulators. Signal Processing Journal 91, 974–984 (2011)
Khoukhi, A., Baron, L., Balazinski, M., Demirli, K.: A Hierarchical Neuro-Fuzzy System to Near Optimal-time Trajectory Planning of Redundant Manipulators. Engineering Applications of Artificial Intelligence 21, 562–570 (2008)
Hammour, Z.S.A., Mirza, N.M., Mirza, S.M., Arif, M.: Cartesian Path Generation of Robot Manipulators using Continuous Genetic Algorithms. Robotics and Autonomous Systems 41, 179–223 (2002)
Pozna, C., Troester, F., Precup, R.E., Tar, J.K., Preitl, S.: On the design of an obstacle avoiding trajectory: Method and simulation. Mathematics and Computers in Simulation 79, 2211–2226 (2009)
Angeles, J.: Fundamental of Robotic: Mechanical Systems: Theory, Methods, and Algorithms. Springer, New York (2002)
Dombre, E., Khalil, W.: Robot Manipulators: Modelling, Performance, Analysis, and Control. ISTE Ltd, London (2002)
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Machmudah, A., Parman, S. (2013). Feasible Joint Angle Continuous Function of Robotics Arm in Obstacles Environment Using Particle Swarm Optimization. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_41
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DOI: https://doi.org/10.1007/978-3-642-30504-7_41
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