Three-dimensional path planning is critical for the successful operation of unmanned aerial vehicles (UAVs), automated guided vehicles (AGVs), and robots in industrial Internet of Things (IIoT) applications. In 3D path planning, the standard Particle Swarm Optimization (PSO) algorithm suffers from premature convergence and a tendency to fall into local optima, leading to significant deviations from the optimal path. This paper proposes an improved PSO (IPSO) algorithm that enhances particle diversity and randomness through the introduction of logistic chaotic mapping, while employing dynamic learning factors and nonlinear inertia weights to improve global search capability. Experimental results demonstrate that IPSO outperforms traditional methods in terms of path length and computational efficiency, showing potential for real-time path planning in complex environments.
Ma et al. (Fri,) studied this question.