Abstract
The main aim of any path/motion planning algorithm in the context of a mobile robot is to produce a collision-/crash-free path among the goal and start points in an environment in which it is present. The past few decades have seen the development of various methodologies to design an optimal path. The present research focuses on the development of an optimized path planning algorithm for the robot using a hybrid method after combining particle swarm optimization (PSO) algorithm with potential field method for static obstacles and potential field method (PFM) prediction for dynamic obstacles. While implementing, PSO-based potential field method, the total potential, that is the sum of repulsive and attractive potentials, is considered as the fitness function which is optimized using PSO algorithm. Further, a 3-point method has been used for smoothing the obtained path. Once the image of the scene is obtained, a clustering method is employed to find the center of obstacle and the location of the robot has been determined by calculating the repulsive potential in each iteration. Finally, the developed algorithms are tested on both the static and dynamic environments in computer simulations and found satisfactory.
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Mandava, R.K., Bondada, S., Vundavilli, P.R. (2019). An Optimized Path Planning for the Mobile Robot Using Potential Field Method and PSO Algorithm. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_11
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DOI: https://doi.org/10.1007/978-981-13-1595-4_11
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