Gathering Data from Risky Situations with Pareto-Optimal Trajectories

This paper proposes a formulation for the risk-aware path planning problem which utilizes multi-objective optimization to dynamically plan trajectories that satisfy multiple complex mission specifications. In the setting of persistent monitoring, we develop a method for representing environmental information and risk in a way that allows for local sampling to generate Pareto-dominant solutions over a receding horizon. We propose two algorithms capable of solving these problems: a dense sampling approach and an improved method utilizing noisy gradient descent. Simulation results demonstrate the efficacy of our methods at persistently gathering information while avoiding risk, robust to randomly-generated environments.