We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a decentralized multi-robot task allocation application for trash collection.

翻译：我们提出了一种阈值决策框架，用于控制智能体在两个空间任务间切换时的物理动态。该框架将描述智能体对特定任务偏好演化的非线性观点动力学模型与智能体的物理动态相耦合。我们证明了支配耦合动态行为的分岔现象，并通过分岔行为展示了耦合动态如何适应智能体的物理约束。我们还揭示了如何通过调节分岔使智能体能够根据自适应于环境条件的阈值进行任务切换。通过一个用于垃圾收集的分布式多机器人任务分配应用实例，我们阐述了该方法的优势。