Abstract
Planning the shortest path for an autonomous mobile robot is a challenging problem. It consists in designing for the robot the optimal path to join the ending location from a starting position with avoiding the obstacle spaces. In this paper, the innovative greedy Dhouib-Matrix-SPP (DM-SPP) method is proposed to address this problem in a static environment with a grid model representation. At first, the grid model is codified as a graph using the eight movement directions where the maximal time complexity of DM-SPP for this problem is O(9*n) with n is the number of nodes in the graph. Then, DM-SPP uses this graph to rapidly search for the shortest path between the initial and the goal nodes. Hence, DM-SPP is developed with Python language and the planning path is illustrated using the Matplotlib library. Finally, experiments on eleven grid models with comparison on twelve metaheuristics (such as Particle Swarm Optimization integrated with Gray Wolf Optimization, Ant Colony Optimization combined with A* method and other metaheuristics) are studied and the results demonstrated the rapidity and the effectiveness of DM-SPP in designing the shortest path for the autonomous mobile robot. Clearly, DM-SPP improves massively the running time and the quality compared to all the other techniques and it can be concluded that DM-SPP is the fastest artificial intelligence method for the Mobile Robot Path Planning Problem.
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