Monte Carlo Simulation of Dynamic Systems on GPU’s

Rogers, Jonathan

doi:10.1007/978-3-319-06548-9_15

Cited by 1 publication

(2 citation statements)

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“…While historically Monte Carlo algorithms have suffered from poor computational scalability and real-time implementation has been considered to be infeasible, new advancements in embedded massively parallel computing now allow Monte Carlo to be performed up to two orders of magnitude faster than on comparable serial processors. 24,25 This performance improvement has been established even for 6DOF vehicle simulations (for instance, see Ilg et al 26 ). Furthermore, a similar Monte Carlo based guidance law to that considered here was recently flight tested by the authors using an embedded GPU onboard a small autonomous parafoil system, demonstrating real-time execution of Monte Carlo uncertainty propagation.…”

Section: Implementation Considerationsmentioning

confidence: 88%

Section: Probabilistic Path Planning Algorithmmentioning

confidence: 91%

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A sampling-based probabilistic path planner for multirotor air vehicles in cluttered environments

Brown

Rogers

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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Successful navigation of small, unmanned aerial vehicles (UAVs) in cluttered environments is a challenging task, especially in the presence of turbulent winds and state estimation uncertainty. This paper proposes a probabilistic path planner for UAVs operating in cluttered environments. Unlike previous sampling-based approaches which select robust paths from a set of trajectory candidates, the proposed algorithm seeks to modify an initial desired path so that it satisfies obstacle avoidance constraints. Given a desired path, Monte Carlo uncertainty propagation is performed and obstacle collision risks are quantified at discrete intervals along the trajectory. A numerical optimization algorithm is used to modify the flight path around obstacles and reduce probability of collision while maintaining as much of the originally desired path as possible. The proposed path planner is specifically designed to leverage embedded massively parallel computers for near real-time uncertainty propagation. Thus the planner can be run in real-time in a feedback manner, modifying the path appropriately as new measurements are obtained. Example results for a standard quadrotor show the ability of the path planning scheme to successfully generate trajectories in cluttered environments. Trade studies characterize algorithm performance as a function of obstacle density and collision risk acceptability.

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Section: Implementation Considerationsmentioning

confidence: 88%

Section: Probabilistic Path Planning Algorithmmentioning

confidence: 91%