Optimal Robot Path Planning Using Gravitational Search Algorithm
This paper proposes a new Gravitational Search Algorithm (GSA)-based approach for generating an optimal path for a robot travelling in partially unknown environments in the presence of multiple (static or dynamic) obstacles. The GSA-based approach is expressed as an algorithm which computes an optimal path for a robot that travels from an initial point to a target point while avoiding all the known obstacles in the environment but also any other static or dynamic object that could appear in the path of the robot to the target point. To validate the new approach for the path planning, the new algorithm is employed in the generation of obstacle-free paths for different robots that are participating in different missions in the framework of the nRobotic platform developed at the “Politehnica” University of Timisoara, Romania. A comparison focused on the resulted path length and performance with another well-known evolutionary algorithm represented by the Particle Swarm Optimization used for path planning is performed.
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