为复杂环境探索设计自适应机器人任务分配策略
Capability-aware Task Allocation and Team Formation Analysis for Cooperative Exploration of Complex Environments
- 基于马尔可夫决策过程建模异构机器人协作探索
- 在真实地下挑战赛场景中提升任务完成率与奖励
- 提供异构团队构建的权衡分析，适合机器人系统设计者
为实现复杂现实场景中探索任务的自主化，本文研究具有异构自主能力的机器人团队部署策略。将环境描述、机器人能力与任务结果建模为马尔可夫决策过程（MDP），并考虑传感器故障、通信覆盖有限及移动性压力等实际约束。在DARPA地下挑战赛（SubT Challenge）的真实场景下验证所提操作模型，并与决赛中人类制定的策略进行对比。结果表明，该策略显著提升团队生产力与任务收益。最后，基于模型分析异构机器人团队构建中的设计权衡，为多机器人系统配置提供理论依据。
原文摘要 · Abstract (English)
To achieve autonomy in complex real-world exploration missions, we consider deployment strategies for a team of robots with heterogeneous autonomy capabilities. In this work, we formulate a multi-robot exploration mission and compute an operation policy to maintain robot team productivity and maximize mission rewards. The environment description, robot capability, and mission outcome are modeled as a Markov decision process (MDP). We also include constraints in real-world operation, such as sensor failures, limited communication coverage, and mobility-stressing elements. Then, we study the proposed operation model on a real-world scenario in the context of the DARPA Subterranean (SubT) Challenge. The computed deployment policy is also compared against the human-based operation strategy in the final competition of the SubT Challenge. Finally, using the proposed model, we discuss the design trade-off on building a multi-robot team with heterogeneous capabilities.
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