CAREER: Decentralized and Online Planning for Emergent Cooperation in Multi-Robot Teams

职业：多机器人团队紧急合作的去中心化在线规划

• 批准号: 2235622
• 负责人: Alyssa Pierson
• 金额: $ 60万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235622&HistoricalAwards=false
• 关键词: CAREER; Decentralized; Online; Planning; Emergent

Mobile robot teams can address needs for in-home service, personal mobility, warehouse management, and agricultural monitoring. These applications require robots to work in complex, dynamic, and cluttered environments. Further, robots need to interact with other robots and humans. Understanding nuances in how robots interact with other robots allows for safer and more robust systems. State-of-the-art research often defines teams of robots as cooperative or non-cooperative, with fixed relationships and interactions within the team. However, this limits the capabilities of the robot team. This Faculty Early Career Development (CAREER) project envisions robot teams that adapt to others with emergent cooperation. Robots may change their cooperation based on their task, surroundings, and the decisions of others. Creating these robot teams requires new insight into how they make decisions. This project explores reputation, reciprocity, and co-existence for a robot team, which enable emergent cooperation. Equipping robots with these skills creates safer and more robust teams of mobile service robots.This project investigates how to integrate underlying cooperation and interaction models into the design of a heterogeneous mobile robot team. Integral to this research are new types of geometric control policies that incorporate uncertainty and future predictions of other robots. Research on (1) reputation, (2) reciprocity, and (3) co-existence work towards the long-term goal of enabling emergent cooperation. Leveraging new geometric tools for environmental decompositions allows for fast and decentralized decision-making among robots, and combining this with game theory and nonlinear control allows the robots to reason about future actions. This project will develop new distributed and decentralized control algorithms with provable properties and demonstrate their performance in simulations and hardware experiments. The first objective focuses on predicting the reputation of other robots. Estimating reputation allows robots to partition an environment based on the predicted ability of others, adjusting to variations without direct communication. The second objective defines reciprocity, which allows robots to predict and negotiate local interactions based on historical interactions and improves the overall efficiency across teams. The third objective explores co-existence, which allows robots to predict conflict with other teams in shared environments and modify policies to reduce conflict while still fulfilling their goals. The performance of these policies will be analyzed through cooperative game theory and Lyapunov stability theory, allowing for provably quantifying the performance gains of robot teams. Ultimately, this enables a team of robots to cooperate with an unknown group of robots within a cluttered environment. Example applications include delivery robots and resource-foraging robots.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动的机器人团队可以满足家庭服务、个人移动性、仓库管理和农业监测的需求。这些应用要求机器人在复杂、动态和混乱的环境中工作。此外，机器人需要与其他机器人和人类进行交互。了解机器人如何与其他机器人交互的细微差别可以实现更安全，更强大的系统。最先进的研究通常将机器人团队定义为合作或非合作，团队内有固定的关系和互动。然而，这限制了机器人团队的能力。这个教师早期职业发展（CAREER）项目设想机器人团队通过紧急合作来适应他人。机器人可能会根据他们的任务，环境和其他人的决定来改变他们的合作。创建这些机器人团队需要对他们如何做出决策有新的见解。这个项目探讨了机器人团队的声誉，互惠和共存，使紧急合作。装备这些技能的机器人创建更安全和更强大的移动的服务robots.This项目研究如何集成底层的合作和交互模型到异构移动的机器人团队的设计。这项研究的组成部分是新类型的几何控制策略，其中包括不确定性和其他机器人的未来预测。研究（1）声誉，（2）互惠，（3）共存工作的长期目标，使紧急合作。利用新的几何工具进行环境分解，可以在机器人之间进行快速和分散的决策，并将其与博弈论和非线性控制相结合，使机器人能够推理未来的行动。该项目将开发具有可证明属性的新的分布式和分散式控制算法，并在仿真和硬件实验中展示其性能。第一个目标是预测其他机器人的声誉。估计声誉允许机器人根据预测的其他人的能力来划分环境，在没有直接通信的情况下调整变化。第二个目标定义了互惠性，它允许机器人基于历史交互来预测和协商本地交互，并提高团队的整体效率。第三个目标是探索共存，它允许机器人预测与共享环境中其他团队的冲突，并修改策略以减少冲突，同时仍然实现其目标。这些政策的性能将通过合作博弈论和李雅普诺夫稳定性理论进行分析，允许可证明量化的机器人团队的性能收益。最终，这使得一组机器人能够在混乱的环境中与一组未知的机器人合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Covering Dynamic Demand with Multi-Resource Heterogeneous Teams

用多资源异构团队满足动态需求

• DOI: 10.1109/iros55552.2023.10342119
• 发表时间: 2023
• 期刊: IEEE
• 作者: Coffey, Mela;Pierson, Alyssa
• 通讯作者: Pierson, Alyssa