stut 11 Robot Path Planning in Unknown Environments Using Particle Swarm Optimization Leandro dos Santos Coelho and Viviana Cocco Mariani

stut 2 2 Contents Abstract 1 Introduction 2 Novel path planning method based on PSO 3 Simulations 4 5 Conclusion 6 Reference

stut 3 3 Abstract  The positions of globally best particle in each iterative are selected, and reached by the robot in sequence  The optimal path is generated with this method when the robot reaches its target

stut 4 4 Introduction Introduction  Mobile robots have been successfully used in many fields due to their abilities to perform difficult tasks in hazardous environments  Robot path planning is to generate a collision- free path in an environment

stut 5 5 Novel path planning method based on PSO (1/4)  In particle swarm optimization particles communicate with each other while learning their own experience  The problem space is initialized with random solutions in which the particles search for the optimum

stut 6 6 Novel path planning method based on PSO (2/4)  The PSO velocity and position update equations

stut 7 Novel path planning method based on PSO (3/4)  the robot possesses the information of the target and the obstacles being detected during the path planning  The procedure of real-time path planning is divided into the following three steps stut 7

8 8 Novel path planning method based on PSO (4/4)  Here we take 2-norm, expressing the traditional distance between two points in 2-dimensional

stut 9 9 Simulations(1/6)  We firstly consider the case that there is only one moving obstacle in the environment  It can be seen that the robot reaches the target without colliding with the obstacle

stut 10 stut 10 Simulations(2/6)

stut 11 stut 11 Simulations(3/6)  In this case we consider a robot path planning in a more complex dynamic environment  The simulation scenario of the second case is shown as Figure 2

stut 12 stut 12 Simulations(4/6)

stut 13 stut 13 Simulations(5/6)  In the third case we consider two robots path planning in a dynamic environment  In the simulation the two robots deal with the moving and static obstacles in the search space with the same rules demonstrated in the second simulation

stut 14 stut 14 Simulations(6/6)

stut 15 stut 15 Conclusion  We regard the problem of robot path planning as an optimization problem and solve it with PSO Although the programmed path has some optimal effect by using the information of the target and obstacles simultaneously it is a very difficult problem to be solved

stut 16 stut 16 Reference Reference  [1] Latombe, J.C.: Robot Motion Planning. Kluwer, Norwell,MA (1991)  [2] Wang, Y.J., Lane, D.M., Falconer, G.J.: Two Novel Approaches for Unmanned Underwater Vehicle Path Planning: Constrained Optimization and Semi-infinite Constrained Optimization.Robotica. 18, 123–142 (2000)  [3] Rimon, E., Doditschek, D.E.: Exact Robot Navigation Using Artificial Potential Fields. IEEE Trans. on Robotics and Automation,vol.8, pp. 501–518 (1992)  [4] Sugihara, K., Smith, J.:Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot. In: Proc. Of IEEE Intl. Symposium on Computational Intelligence in Robotics and Automation, pp. 138–143 (1997)  [5] Sugawara, K., Kazama, T., Watanabe, C.: Foraging Behavior of Interacting Robots with Virtual Pheromone. In: Proc.IEEE/RSJ International Conference on Intelligent Robots and Systems,Vol.3, pp. 3074–3079 (2004)

stut 17 Reference Reference  [6] Yang, S.X., Meng, M.: Neural Network Approaches to Dynamic Collision- Free Trajectory Generation. IEEE Trans. on Systems,Man, and Cybemetic, Vol.31, No.3 (2001)  [7] Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. of the 6th Int. Symp. on Micro Machine and Human Science, pp. 39–43. Nagoya, Japan (1995)  [8] Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization.In: Proc.of IEEE Int. Conf. on Neural Network, pp. 1942–1948.Perth, Australia (1995)  [9] Doctor, S., Venayagamoorthy, G.K.: Unmanned Vehicle Navigation Using Swarm Intelligence. In: Proc. of Int. Conf.on Intelligent Sensing and Information Processing, pp. 249–253(2004)  [10] Xin, C., Li, Y.M.: Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization. In: Proceedings of the 2006 IEEE International Conference on Mechatronicsand Automation, pp. 1722–1727. Luoyang, China (2006)

stut 18 Have a nice day !