论文标题
多机器人系统的分散任务和路径计划
Decentralized Task and Path Planning for Multi-Robot Systems
论文作者
论文摘要
我们考虑了一个多机器人系统，该系统具有由协作机器人组成的团队和随着时间的推移出现的多个任务。我们提出了一个完全分散的任务和路径计划（DTPP）框架，该框架由任务分配模块和局部路径计划模块组成。每个任务都被建模为马尔可夫决策过程（MDP）或混合观察到的马尔可夫决策过程（MOMDP），具体取决于是否可以观察到完整状态还是部分状态。然后，任务分配模块旨在最大限度地提高机器人团队的预期纯奖励（奖励减去成本）。我们将Markov模型融合到一个因子图公式中，以便可以使用MAX-SUM算法来分散任务分配。每个机器人代理都遵循Markov模型合成的最佳策略，我们提出了一种局部的前向动态编程方案，该方案解决了代理之间的冲突并避免碰撞。提出的框架通过高保真性ROS模拟和具有多个地面机器人的实验证明。
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph formulation so that the task allocation can be decentrally solved using the max-sum algorithm. Each robot agent follows the optimal policy synthesized for the Markov model and we propose a localized forward dynamic programming scheme that resolves conflicts between agents and avoids collisions. The proposed framework is demonstrated with high fidelity ROS simulations and experiments with multiple ground robots.
