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

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.

2023 International Conference on Unmanned Aircraft Systems (ICUAS)

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Cited by in corpus (4)

• Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents
• VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation
• Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning
• Probabilistically Robust Trajectory Planning of Multiple Aerial Agents