Distributed collaborative search path planning method for heterogeneous unmanned systems in underground space

Abstract:

To address the problem of inefficient collaborative area search by heterogeneous unmanned systems in underground spaces, this paper comprehensively considers both aerial and ground obstacles and constructs a three-dimensional grid model of the underground space. Based on this, using a three-dimensional sensor model for unmanned systems with adaptive height, the impact of detection distance on detection performance is quantitatively analyzed. Furthermore, by using the pheromone map mechanism, the environmental information is dynamically updated through the diffusion and evaporation of pheromones. Within the framework of distributed model predictive control (DMPC), an improved artificial lemming algorithm-pheromone map (IDALA-PM) is proposed, which integrates differential variation, triangular walk, Gaussian perturbation, and t-distribution adaptive perturbation strategies, to achieve distributed real-time path planning for heterogeneous unmanned aerial and ground systems. Simulation results indicate that the proposed IDALA-PM algorithm can effectively accomplish underground space search tasks and improves search efficiency by more than 54.2% compared to traditional algorithms.