Real-Time Stochastic Optimal Control for Multi-agent Quadrotor Systems

Vicenç Gómez, Sep Thijssen, Andrew Symington, Stephen Hailes and Hilbert J. Kappen

Accepted in 26th International Conference on Automated Planning and Scheduling, London, UK

Abstract

This paper presents a novel method for controlling teams of unmanned aerial vehicles using Stochastic Optimal Control (SOC) theory. The approach consists of a centralized high-level controller that computes optimal state trajectories as velocity sequences, and a platform-specific low-level controller which ensures that these velocity sequences are met. The high-level control task is expressed as a centralized path integral control problem, for which optimal control computation corresponds to a probabilistic inference problem that can be solved by efficient sampling methods. Through simulation we show that our SOC approach (a) has significant benefits compared to deterministic control and other SOC methods in multimodal problems with noise-dependent optimal solutions, (b) is capable of controlling a large number of platforms in real-time, and (c) yields collective emergent behavior in the form of flight formations. Finally, we show that our approach works for real platforms, by controlling a team of three quadrotors.

Paper [pdf]

Supplementary video

Last modified on 15 Febr 2015 by Vicenç Gómez