Experimental investigation of stochastic parafoil guidance using a graphics processing unit. (March 2015)

• Record Type: Journal Article
• Authors: Slegers, Nathan; Brown, Andrew; Rogers, Jonathan
• Is Part Of: Control engineering practice. Volume 36(2015)
• Page Start: 27
• Page End: 38
• Publication Date: 2015-03
• Subjects: Graphics processing unit -- GPU -- Optimal control -- Parallel processing -- Parafoil; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2014.12.002
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the stochastic guidance law compared to standard deterministic guidance schemes, particularly with respect to obstacle avoidance. Flight test results are presented comparing the proposed stochastic guidance algorithm with a standard deterministic one. Through a comprehensive set of simulation results, key implementation aspects of the stochastic algorithm are explored including tradeoffs between the number of candidate trajectories considered, algorithm runtime, and overall guidance performance. Overall, simulation and flight test results demonstrate that the stochastic guidance scheme provides a more robust approach to obstacle avoidance while largely maintaining delivery accuracy. Highlights: A stochastic parafoil guidance system has been experimentally investigated. The guidance algorithm uses an on-board GPU for real-time optimization. Results show favorable correlation with simulation results. Work represents first use of on-board GPU for real-time optimization in robotics.