Abstract
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.
Highlights
Path planning is an essential topic in the robotics field due to the popularity of mobile robots in different applications such as military, industry, libraries, and security
Particle swarm optimization (PSO)-based path planning algorithm has been demonstrated in dynamic environments as presented in the studies of Rath and Deepak,[9] Badmos et al.,[10] and Adamu et al.,[11] whereas multi-objective particle swarm optimization (PSO)-based different application are presented in the studies of Zhang et al and Song et al.[12,13,14,15]
The suggested Modified Frequency Bat (MFB) and Hybrid PSO-MFB algorithms are integrated with the local search (LS) procedure to plan a path far from the obstacles and convert the invalid solutions into valid ones
Summary
Path planning is an essential topic in the robotics field due to the popularity of mobile robots in different applications such as military, industry, libraries, and security. Modified simulated annealing is implemented as a path planner in small UAVs in the work of Behnck et al.[27] Path planning based on hybridization between different swarm optimization algorithms using multi-objective measures has been studied by Ajeil et al.[28] Basaca-Preciado et al.[29] proposed a highaccuracy localization based on dynamic triangulation, where a shorter and smoother trajectory for Pioneer 3-AT mobile robot is obtained. It should be noted that mobile robot navigation including path planning can be regarded as a high-level motion planning task through which the mobile obtains data and reacts to its surroundings. Many linear and nonlinear control techniques are available for the design of the motion control layer.[37,38,39,40,41,42,43]
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