Abstract
Path planning is one of the widely studied problems in mobile robotics, which deals in finding an optimal path for a robot. To generate a collision-free path for a robot in an environment by satisfying certain constraints is a complex task. So, path planning is an NP-hard problem. In this paper, we present a new formulation of the path planning problem for a mobile robot by introducing zones which are neighbors to the static obstacles, through which a robot can pass avoiding the collisions. Our proposed model has an advantage of shrinking the search space which in turns reduces the computational complexity. We consider the minimization of the travel distance of a robot as the main objective to find a feasible path. We implement a genetic algorithm (GA) as a solution technique and compare it with two other well-studied meta-heuristic algorithms, viz. Tabu Search and Simulated Annealing. Further, we incorporate a modified mutation operation in all three algorithms to replace a zone from the reduced search space to generate a new potential solution. The simulations for different environments and comparative analysis using obtained results show that GA performs better than the other two approaches.
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Sumanth Bhaskar, B.G., Rauniyar, A., Nath, R., Muhuri, P.K. (2021). Zone-Based Path Planning of a Mobile Robot Using Genetic Algorithm. In: Chakrabarti, A., Arora, M. (eds) Industry 4.0 and Advanced Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5689-0_23
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DOI: https://doi.org/10.1007/978-981-15-5689-0_23
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