Performance Analysis of Robotic Path Planning Algorithms in a Deterministic Environment
Motion planning is one of the fundamental challenging problems in the field of robotics. The objective is to find the shortest path traversed by the robot encountering any type of obstacles in the space. A number of intelligent path planning algorithms have been designed for solving motion planning problems. This paper presents the performance analyses of various robotic path planning algorithms for avoiding obstacles in a deterministic environment both quantitatively and qualitatively. This paper also discusses the benefits and drawbacks of each method and shows the simulation results of each algorithm on the explored space. These algorithms are tested under various situations with different degrees of complexities. The results analysis shows that with respect to time and path length, Rapidly Exploring Random Tree (RRT) algorithm perform better in finding the optimal path and reaching the specified target point in a given planning horizon.
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