Multirobot Cooperative Path Optimization Approach for Multiobjective Coverage in a Congestion Risk Environment
研究了有障碍和拥堵风险环境下的多机器人任务分配与路径优化问题，提出了改进的PRM*算法和蚁群系统PRM*算法，通过仿真和实验验证了方法的有效性。
This article examines the problems of task allocation and path optimization for multiobjective coverage in a congestion risk environment with obstacle constraints. An improved probabilistic roadmap (PRM*) algorithm is proposed, which eliminates the zig-zag paths around the path endpoints. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -distance PRM* <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(K$ </tex-math></inline-formula> -DPRM*) provides a novel clustering metric for task allocation in an obstacle environment. An ant colony system-PRM* (ACS-PRM*) algorithm is proposed to solve the congestion avoidance traveling salesman problem (CATSP) by voyage optimization of multiobjective coverage. Additionally, the mapping relationship between the probability of environmental congestion and the velocity of robot is established and combined with the feedforward control method to improve the motion control of robots. Simulations and experiments verify the effectiveness of the path optimization method in obstacle environments with congestion risk.