多智能体在动态复杂环境中协同完成任务并规划安全路径。
Task Allocation and Motion Planning in Dynamic, Cluttered Environments via CBBA and Graphs of Convex Sets
- 用凸集图（GCS）构建时空融合的轨迹优化模型。
- 结合共识束算法（CBBA）实现分布式任务分配与实时决策。
- 可避免碰撞并精准预估任务完成时间，适合动态场景应用。
在复杂、动态环境中进行多智能体任务规划，需同时为智能体分配任务并生成安全、高效的运动轨迹。当任务具有动态性（如汇合目标）时，任务分配不仅取决于哪个智能体最适合执行，还取决于任务何时何地可被到达。本文提出一种结合凸集图（Graphs of Convex Sets, GCS）与共识束算法（Consensus-Based Bundle Algorithm, CBBA）的解决方案。其中，GCS通过扩展至三维空间加时间维度的配置空间，优化动态环境中的最优轨迹；而CBBA则在各智能体间协调任务分配，支持在移动环境中做出明智决策。我们将任务分配与路径规划相耦合，使智能体能在三维加时间的配置空间中避障，并准确估算任务完成时间。实验在包含静态与动态任务的模拟复杂环境中验证了该方法的有效性。
原文摘要 · Abstract (English)
Multi-agent task planning in cluttered, dynamic environments requires assigning tasks to agents while simultaneously determining safe, time-efficient trajectories through the environment. When tasks are dynamic, such as rendezvous objectives, allocation decisions depend not only on which agent is best suited for a task, but also on when and where that task can be reached. This paper presents a solution to this problem, which combines Graphs of Convex Sets (GCS) for trajectory optimization with the Consensus-Based Bundle Algorithm (CBBA) for distributed task allocation. In our approach, GCS finds optimal trajectories through dynamic environments using a time-extended (3D+time) configuration space. At the same time, CBBA coordinates task assignments across agents, enabling informed decision-making in a moving environment. We then connect allocation and planning to allow the agents to avoid collisions in the 3D+time configuration space and provide accurate time estimates for task completion. We demonstrate the effectiveness of our approach in simulated cluttered environments with static and dynamic tasks.
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