基于约束优化的多智能体协同编队与避障

Multi-agent Collaborative Formation with Obstacle Avoidance Based on Constrained Optimization

在多智能体系统的研究与实践中,编队路径规划尤为关键,不仅保障智能体的安全运行,也能提高系统执行任务的效率。本文提出了一种适应动态复杂环境的多智能体编队路径规划方法,确保系统在动态避障和编队形状保持方面的实时响应能力。开发了两种核心算法:一种致力于局部运动规划,另一种聚焦于全局路径规划。局部运动规划算法迭代应用半定规划和二次规划,求解得到智能体周围的无障碍凸区域,并通过连续凸优化技术优化编队参数。这一方法有效解决了多智能体编队在动态障碍物避让方面的问题,确保在遵守环境约束的同时实现编队的稳定保持。全局路径规划阶段进一步采用了这一思想,对自由空间中的无障碍凸区域进行采样,结合约束优化来计算起始编队和目标编队之间的过渡编队,并利用图搜索算法找到通往目标编队的最优路径。本文利用MATLAB搭建一个动态障碍物与静态障碍物并存的复杂仓储环境,验证了方法的有效性,并与虚拟结构法和概率路线图(Probabilistic roadmap,PRM)进行对比,展示了其在效率和准确性方面的优越性。

In the research and practice of multi-agent systems,formation path planning is particularly crucial,as it not only ensures the safe operation of agents but also enhances the efficiency of the system in task execution.This study introduces a multi-agent formation path planning method adapted for dynamic and complex environments,ensuring the system’s real-time responsiveness in dynamic obstacle avoidance and formation shape maintenance.This paper develops two core algorithms:One is dedicated to local motion planning and the other is focused on global path planning.The local motion planning algorithm iteratively uses semidefinite programming and quadratic programming to identify obstacle-free convex regions around agents and optimizes formation parameters through continuous convex optimization techniques.This approach effectively addresses the issue of multi-agent formation avoiding dynamic obstacles,ensuring the stability of the formation while adhering to environmental constraints.In the global path planning phase,this concept is further utilized by sampling obstacle-free convex regions in free space,combined with constraint optimization to calculate transitional formations between the initial and target formations,and employing graph search algorithms to find the optimal path to the target formation.This paper utilizes MATLAB to build a complex warehouse environment with both dynamic and static obstacles to verify the effectiveness of the method.It also compares the approach with the virtual structure method and probabilistic roadmap(PRM),demonstrating its superiority in terms of efficiency and accuracy.