Self-triggered Adaptive Planning and Scheduling of UAV Operations

Abstract

Modern unmanned aerial vehicles (UAVs) rely on constant periodic sensor measurements to detect and avoid obstacles. However, constant checking and replanning are time and energy consuming and are often not necessary especially in situations in which the UAV can safely fly in uncluttered environments without entering unsafe states. Thus, in this paper, we propose a self-triggered framework that leverages reachability analysis to schedule the next time to check sensor measurements and perform replanning while guaranteeing safety under noise and disturbance effects. Further, we relax sensor checking and motion replanning operations by leveraging a risk-based analysis that determines the likelihood to reach undesired states over a certain time horizon. We also propose an online speed adaptation policy based on the planned trajectory curvature to minimize drift from the desired path due to the system dynamics. Finally, we validate the proposed approach with simulations and experiments for a quadrotor UAV motion planning case study in a cluttered environment.