针对异构机器人能量差异，设计动态分配覆盖任务的分布式控制算法。
Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System
- 基于Lloyd算法，根据各机器人能量状态动态调整任务权重。
- 实测与仿真表明，该方法显著提升多机器人系统整体覆盖效率。
- 适用于无人机等能量消耗差异大的异构机器人协同场景。
我们提出一种用于异构多机器人覆盖问题的分布式控制律，考虑机器人因尺寸、速度、能力及负载不同而产生的能量特性差异，如能量容量和耗电速率。现有能量感知覆盖控制方法虽考虑容量差异，但假设所有机器人耗电速率相同。然而在实际中，部分机器人（如无人机）可能因任务需求导致能耗差异显著，例如在不同高度悬停时耗电速率可动态变化。为最大化多机器人系统性能，需同时考虑能量容量与耗电速率。为此，我们基于Lloyd算法设计了一种新的能量感知控制器，依据机器人能量动态调整权重并合理划分作业区域。该控制器经理论分析，并通过多场景仿真与真实实验验证，与三种基线控制方法对比，结果证明其有效性和优越性。
原文摘要 · Abstract (English)
We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws consider capacity differences but assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots; for instance, UAVs hover at different altitudes, and these changes could be dynamically updated based on their assigned tasks. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy dynamics and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations and real-world demonstrations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy.
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