Abstract
Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable cooperative transport. However, allocating transportation tasks of objects with unknown weights remains challenging. Moreover, the presence of infeasible tasks (untransportable objects) can lead to robot stoppage (deadlock). This paper proposes a framework for dynamic task allocation that involves storing task experiences for each task in a scalable manner with respect to the number of robots. First, these experiences are broadcasted from the cloud server to the entire robot system. Subsequently, each robot learns the exclusion levels for each task based on those task experiences, enabling it to exclude infeasible tasks and reset its task priorities. Finally, individual transportation, cooperative transportation, and the temporary exclusion of tasks considered infeasible are achieved. The scalability and versatility of the proposed method were confirmed through numerical experiments with an increased number of robots and objects, including unlearned weight objects. The effectiveness of the temporary deadlock avoidance was also confirmed by introducing additional robots within an episode. The proposed method enables the implementation of task allocation strategies that are feasible for different numbers of robots and various transport tasks without prior consideration of feasibility.
Abstract (translated)
多机器人系统利用多机器人传输具有实现多种实际应用潜力，如配送服务。然而，分配未知重量的对象的运输任务仍然具有挑战性。此外，存在不可行任务（无法运输的对象）可能会导致机器人停止（死锁）。本文提出了一种动态任务分配框架，其中将每个任务的任务经历以可扩展的方式存储在机器人数量上。首先，这些经历从云服务器传播到整个机器人系统。然后，根据任务经历学习每个机器人任务的排除级别，使它能够排除不可行任务并重置任务优先级。最后，通过实现个体传输、合作传输和暂时排除任务，实现了所需的任务传输。通过增加机器人数量和对象，包括未学习的重量对象，验证了所提出的可扩展性和多样性。临时避免死锁的效果也得到了证实，通过在单个 episode 中引入额外的机器人。所提出的方法使不同机器人数量和各种传输任务下实现任务分配策略成为可能，而无需事先考虑可行性。
URL
https://arxiv.org/abs/2404.11817
