Persistent Monitoring Trajectory Optimization in Partitioned Environments

We consider the problem of using an autonomous agent to persistently monitor a collection of targets distributed in a given environment. We generalize existing work by allowing the agent’s dynamics to vary throughout the environment, leading to a hybrid dynamical system. This introduces an additional layer of complexity towards the planning portion of the problem: we must not only identify in which order to visit the points of interest, but also in which order to traverse the regions. We propose a tailored global path planner and prove that it is not only probabilistically complete, but converges in probability to a time-optimal solution. We then design an offline sequence planner together with an online trajectory optimizer. Simulations validate the results.