Synergistic Performance Enhancement in Heterogeneous Robot Teams

这项研究解决了一个可以对自主机器人团队的应用产生变革性影响的基本问题：“在现实世界条件下，异质机器人团队如何通过向同伴、人员和环境学习来协同提高他们的表现？”尽管多机器人团队的研究和发展取得了进展，但一个关键的挑战仍然是如何开发有效的机制，使机器人能够在现实生活中自主地产生、适应和增强群体行为，同时提高它们的个人表现。这项研究的目标是通过扩展社会行为在一个不同的机器人团队中所扮演的角色，在现实世界的条件下，通过研究和发展群体表现增强的关键方面，来提高自主性。它试图通过适当的机制来理解和实施人类学习如何在不确定和非结构化的情况下增加他们对团队合作的贡献的能力，不仅要提高他们自己的表现，还要提高他们通过有效的建议和批评向他人传递知识和经验的能力。该研究的前提是，这种计算机制可以在现实场景中有效，主要是通过模块化、可扩展和分层控制体系结构中所有学习层的协同集成，从而实现动态分布式处理、通信、任务分配和协调以及故障恢复。利用这种体系结构，提出了一种多层的团队学习分解方法。该方法将考虑集体（个人）、合作（任务分配和协调）和协作（建议共享）三层学习行为，并试图同时开发分布式马尔可夫决策过程作为这三层的学习问题模型。此外，人类顾问对群体性能增强的贡献，以来自个人和/或匿名人群的标记和未标记训练数据的形式，将通过增强的人类建议层来考虑。最后，探讨了状态不确定性和团队/任务重构的问题，并利用基于粒子滤波的状态估计器对开发的并发马尔可夫决策过程进行了增强，形成了鲁棒的群体性能增强机制。目标是使机器人能够处理各种情况，例如感官噪声，不同的通信带宽和间歇性断开，以及并发的软件和硬件故障，执行各种团队任务，包括基本导航和绘图，避免碰撞，路径规划，目标发现，在未知的混乱环境中觅食和放牧。

This research addresses a fundamental question that can have a transformative impact on the applications of autonomous robot teams: “how can a team of heterogeneous robots synergistically enhance their performance through learning from peers, people, and environment, under real-world conditions?” Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt and enhance group behaviours in real-life situations, while improving their individual performance concurrently. The goal of the research is to advance autonomy by expanding upon the role that social behaviour can play in a heterogeneous team of robots, under real-world conditions, and through studying and developing the key aspects of group performance enhancement. It attempts to comprehend and implement, through proper mechanisms, the human’s capability of learning how to increase their contributions towards teamwork, in uncertain and unstructured scenarios, by enhancing not only their own performance but also their ability to transfer knowledge and experience to others through effective advice and criticism. The premise of the research is that such computational mechanisms can be effective in real-life scenarios mainly through a synergistic integration of all learning layers within a modular, scalable and hierarchical control architecture, which allows for dynamically distributed processing, communication, task allocation and coordination, and failure recovery. Through the utilization of such architecture, a multi-layered approach is proposed to the team learning decomposition problem. The approach will consider three layers of collective (individual), cooperative (task allocation and coordination) and collaborative (advice sharing) learning behaviours, and attempt to develop distributed Markov decision processes as learning problem models for the three layers concurrently. Further, the contribution of human advisors to group performance enhancement, in the form of labelled and unlabeled training data from individuals and/or anonymous crowd, will be considered through an augmenting human advice layer. Finally, the subject of state uncertainty as well as team/task reconfiguration will be explored, and the developed concurrent Markov decision processes will be enhanced with a state estimator, based on a particle filter, to form a robust group performance enhancement mechanism. The objective is to enable the robots to handle situations, such as sensory noise, varying communication bandwidth and intermittent disconnections, and concurrent software and hardware failure, in performing various team tasks, including basic navigation and mapping, collision avoidance, path planning, and object discovery, foraging and herding in an unknown cluttered environment.