UAV Path Planning for Multitarget Tracking with Experiments

Abstract

We apply an optimization-based path planning framework for an unmanned aerial vehicle. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides mobile sensor trajectories that are feasible with respect to nonholonomic vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the state estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming (NLP) problem using the direct transcription method known as simultaneous collocation. A field experiment illustrates the approach.