A Latency-offset Online Trajectory Planner based on Successive Convex Approximation

Abstract

An online trajectory planner permits autonomous unmanned vehicles to maneuver in a changing mission scenario and varying environment. However, in real-time online plannings, task performance, completeness and accuracy is seriously challenged by the latency during mission planning and scheme computation. To address the issue, we propose a latency-correcting online trajectory planner based on successive convex approximation (SCA), which is aiming at offset the latency in the planning. In this work, two types of latency are considered, i.e., a prior known variable and computation, where the latter one poses a problem. With this, we devise an online latency method to predict computing latency by conducting time complexity analysis. The experiment is to design an optimal UAV trajectory to serve the eavesdropping of an uncooperative emitter. The results showed this harmful latency effect, and then demonstrated the improved accuracy and task performance of the proposed planner.