地下空间异构无人系统分布式协同搜索路径规划方法

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

关键词:  地下空间  空地异构无人系统  协同搜索路径规划  DMPC  IDALA-PM

Key words:  underground space  air-space heterogeneous unmanned systems  collaborative search path planning  distributed model predictive control (DMPC)  improved distributed artificial lemming algorithm (IDALA)

摘要:

为解决地下空间中空地异构无人系统协同区域搜索效率低下的问题,本文综合考虑空中与地面障碍物的双重约束,构建了三维栅格地下空间模型。基于此,利用自适应高度的无人系统三维传感器模型,量化分析了探测距离对探测性能的影响,并采用信息素图机制,通过信息素的扩散与挥发动态更新环境信息。在分布式模型预测控制(distributed model predictive control,DMPC)框架下,融合差分变异、三角形游走、高斯扰动和t分布自适应扰动策略,提出了一种融合信息素图机制的改进人工旅鼠算法(improved artificial lemming algorithm-pheromone map,IDALA-PM),以实现多空地异构无人系统的分布式实时路径规划。仿真结果表明,所提出的IDALA-PM算法能够有效完成地下空间搜索任务,相比传统算法,搜索效率提高了54.2%。

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.