任务分配可行性的集体决策
Collective Decision-Making on Task Allocation Feasibility
AI总结：
针对机器人群组任务可行性评估难题，提出分布式可行性概念，采用基于多数决策的直接调制策略，实现同构场景下非临界机器人任务比时的可行性集体判定。
摘要：
机器人群组有望为现实应用带来诸多优势，但部署这类群组时，如何确保其在多样环境中的可行性仍存挑战。评估群组执行新任务的可行性，对于保障资源高效利用、明确群组方案对特定任务的适配性至关重要。本文提出分布式可行性概念，即群组基于局部观测与交互，集体评估任务分配的可行性。我们采用“基于多数决策的直接调制（Direct Modulation of Majority-based Decisions）”作为集体决策策略，实验表明在同构场景下，只要机器人与任务的比例不接近1，群组就能集体判定给定配置的可行性高低。
英文摘要：
Robot swarms offer the potential to bring several advantages to the real-world applications but deploying them presents challenges in ensuring feasibility across diverse environments. Assessing the feasibility of new tasks for swarms is crucial to ensure the effective utilisation of resources, as well as to provide awareness of the suitability of a swarm solution for a particular task. In this paper, we introduce the concept of distributed feasibility, where the swarm collectively assesses the feasibility of task allocation based on local observations and interactions. We apply Direct Modulation of Majority-based Decisions as our collective decision-making strategy and show that, in a homogeneous setting, the swarm is able to collectively decide whether a given setup has a high or low feasibility as long as the robot-to-task ratio is not near one.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
