多机器人在大空间中带最大续航约束的任务分配与路径规划
Multi-robot Task Allocation and Path Planning with Maximum Range Constraints
- 基于拍卖机制融合路径规划，动态计算任务奖励
- 考虑障碍物绕行距离，避免理想直线距离误判
- 采用懒惰拍卖策略加速收敛，适合实时多机协同
本文提出一种新型多机器人任务分配与路径规划方法，适用于大尺度工作空间中机器人最大续航约束场景，确保机器人在自身续航范围内完成任务。首先，设计了一种快速全局路径规划器以高效求解路径；随后，提出一种创新的基于拍卖的方法，将路径规划嵌入拍卖阶段进行奖励计算，同时考虑机器人的续航限制。该方法引入实际避障路径长度而非理想直线距离，有效解耦任务分配与路径规划之间的耦合关系。此外，为避免迭代过程中的冗余计算，采用懒惰拍卖策略，显著提升任务分配收敛速度。通过大量仿真和真实世界实验验证了该方法的有效性与应用潜力。代码实现将公开于 https://github.com/wuuya1/RangeTAP。
原文摘要 · Abstract (English)
This letter presents a novel multi-robot task allocation and path planning method that considers robots' maximum range constraints in large-sized workspaces, enabling robots to complete the assigned tasks within their range limits. Firstly, we developed a fast path planner to solve global paths efficiently. Subsequently, we propose an innovative auction-based approach that integrates our path planner into the auction phase for reward computation while considering the robots' range limits. This method accounts for extra obstacle-avoiding travel distances rather than ideal straight-line distances, resolving the coupling between task allocation and path planning. Additionally, to avoid redundant computations during iterations, we implemented a lazy auction strategy to speed up the convergence of the task allocation. Finally, we validated the proposed method's effectiveness and application potential through extensive simulation and real-world experiments. The implementation code for our method will be available at https://github.com/wuuya1/RangeTAP.
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