  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.
