多机器人在复杂障碍环境下的高效全覆盖，靠广义Voronoi图实现分区负载均衡。
A Generalized Voronoi Graph based Coverage Control Approach for Non-Convex Environment
- 用广义Voronoi图划分非凸区域，按质量差异分配机器人
- 迭代优化机器人数量与区域质量匹配，实现负载均衡
- 新控制器保障覆盖收敛，适合复杂场景多机协同
为解决多机器人系统在含多个障碍物的非凸区域内高效覆盖的问题，本文提出一种基于广义Voronoi图（GVG）的覆盖控制方法，包含两阶段：负载均衡算法阶段与协作覆盖阶段。在负载均衡算法阶段，基于GVG将非凸区域划分为多个子区域，并设计加权负载均衡算法，考虑子区域质量差异；通过迭代优化机器人分配比例，使各子区域的机器人数量与其质量相匹配，实现负载均衡。在协作覆盖阶段，每个机器人由新控制器驱动，实现有效覆盖。方法的收敛性得到证明，仿真验证了其性能。
原文摘要 · Abstract (English)
To address the challenge of efficient coverage by multi-robot systems in non-convex regions with multiple obstacles, this paper proposes a coverage control method based on the Generalized Voronoi Graph (GVG), which has two phases: Load-Balancing Algorithm phase and Collaborative Coverage phase. In Load-Balancing Algorithm phase, the non-convex region is partitioned into multiple sub-regions based on GVG. Besides, a weighted load-balancing algorithm is developed, which considers the quality differences among sub-regions. By iteratively optimizing the robot allocation ratio, the number of robots in each sub-region is matched with the sub-region quality to achieve load balance. In Collaborative Coverage phase, each robot is controlled by a new controller to effectively coverage the region. The convergence of the method is proved and its performance is evaluated through simulations.
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