Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

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