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的物理动力学。该框架将表征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力学相耦合。我们证明了主导耦合动力学行为的相变条件，并揭示了如何通过相变行为使耦合动力学自适应于智能体的物理约束。同时展示了如何调节相变过程，使智能体能基于环境条件自适应阈值进行任务切换。通过一个分散式多机器人垃圾收集任务分配应用实例，验证了本方法的有效性。