Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent

TL;DR

This paper introduces a probabilistically robust optimal control framework for UAVs that jointly optimizes motion and camera control for 3D coverage, incorporating logical constraints and the unscented transformation for robustness.

Contribution

It presents a novel hybrid optimal control approach that integrates logical coverage constraints with probabilistic robustness using the unscented transformation.

Findings

Successfully integrates logical constraints into continuous optimization.

Demonstrates robustness to uncertainties via the unscented transformation.

Enables joint optimization of UAV motion and camera control for 3D coverage.

Abstract

We propose a novel probabilistically robust controller for the guidance of an unmanned aerial vehicle (UAV) in coverage planning missions, which can simultaneously optimize both the UAV's motion, and camera control inputs for the 3D coverage of a given object of interest. Specifically, the coverage planning problem is formulated in this work as an optimal control problem with logical constraints to enable the UAV agent to jointly: a) select a series of discrete camera field-of-view states which satisfy a set of coverage constraints, and b) optimize its motion control inputs according to a specified mission objective. We show how this hybrid optimal control problem can be solved with standard optimization tools by converting the logical expressions in the constraints into equality/inequality constraints involving only continuous variables. Finally, probabilistic robustness is achieved by integrating the unscented transformation to the proposed controller, thus enabling the design of robust open-loop coverage plans which take into account the future posterior distribution of the UAV's state inside the planning horizon.