Abstract
Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of non-deterministic polynomial-time hard (NP-hard). Particle swarm optimization (PSO) has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE) algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO), is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self-adaptive DE (RBSADE), is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
Highlights
Over the past few decades, mobile robotics has been successfully applied in industry, military and security environments to perform crucial unmanned missions such as planet exploration, surveillance and landmine detection [39]
Particles first follow the moving rules defined in nonlinear timevarying PSO (NTVPSO) to update their velocities and posi‐ tions
Consequent‐ ly, NTVPSO promotes particles to search for high-quality paths
Summary
Over the past few decades, mobile robotics has been successfully applied in industry, military and security environments to perform crucial unmanned missions such as planet exploration, surveillance and landmine detection [39]. How to set generation strategies and control parameters are problem-dependent, which results in difficulties when designing an efficient DE algorithm [8, 38] Since both PSO and DE are EAs that deal with population evolution, it is natural to mix these two algorithms together to leverage their advantages when developing an integrat‐ ed method for efficiently solving optimization problems. To verify the proposed algorithm for solving mobile robot global path planning, it is compared to JADE [35], the timevarying particle swarm optimization (TVPSO) method [15], the gravitational search (GS) method [18] and the modified genetic algorithm (mGA) [25] under different simulation scenarios.
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