Projectile Monte-Carlo Trajectory Analysis Using a Graphics Processing Unit

Ilg, Mark; Rogers, Jonathan; Costello, Mark

doi:10.2514/6.2011-6266

Cited by 17 publications

(18 citation statements)

References 4 publications

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“…3) Massively-Parallel GPU Predictions-The GPU Monte Carlo algorithm is similar to that proposed in [21], in which the GPU kernel is a 6DoF trajectory propagator. Here, the kernel propagates the 6DoF parafoil model with the complete MPC guidance system that tracks the candidate trajectory.…”

Section: B General Purpose Gpu Based Guidance Solutionmentioning

confidence: 99%

“…The M x N solutions are prescreened using (14) to find the best 390 solutions. In this case, the value 390 is selected because optimal GPU execution occurs when Monte Carlo simulations are run in multiples of the number of multiprocessors on the device [21], which for this particular GPU is 30. Any number of solutions could be selected, however, the prescreened cases will be sent to the GPU model to evaluate statistics through Monte Carlo simulation for each particular combination of turn rate and approach direction.…”

Section: B General Purpose Gpu Based Guidance Solutionmentioning

confidence: 99%

“…GPU's have been leveraged across a wide spectrum of computational problems from molecular dynamics [15,16], incompressible flows [17], and N-body problems [18] to optical flow processing [19] and computational fluid dynamics [20]. Ilg et al [21] recently showed that runtimes of six degree-of-freedom (DoF) Monte Carlo simulations can be reduced by up to an order-of-magnitude when performed on a GPU, creating the possibility that such simulations can be run efficiently as part of a real-time autonomous guidance system. In fact, embedded processors containing on-board graphics accelerators have recently become available [22] which leverage the same processing constructs as desktop GPU's.…”

Section: Introductionmentioning

confidence: 99%

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Robust Parafoil Terminal Guidance Using Massively Parallel Processing

Rogers

Slegers

2012

AIAA Atmospheric Flight Mechanics Conference

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Terminal guidance of autonomous parafoils is a difficult problem in which wind uncertainty and system underactuation are major challenges. Existing strategies almost exclusively use impact error as the criterion for optimality. Practical airdrop systems, however, must also include other criteria that may be even more important than impact error in some missions, such as ground speed at impact or constraints imposed by challenging dropzones. Furthermore, existing guidance schemes determine terminal trajectories using deterministic wind information and may result in a solution that works in ideal wind but may be sensitive to variations. The work described here develops a guidance strategy that uses massively parallel Monte Carlo simulation performed on a graphics processing unit to rank candidate trajectories in terms of robustness to wind uncertainty. Based on the guidance system's confidence in wind estimates, an appropriate trajectory path is chosen that provides a reasonable level of robustness given uncertainty in wind conditions. The result is robust guidance, as opposed to optimal guidance. Through simulation results, the proposed path planning scheme proves more robust in realistic dynamic wind environments compared with previous optimal trajectory planners that assume perfect knowledge of a constant wind. Nomenclature A, B, C = discrete system state space matrices B = discrete model control sensitivity D = distance of the turn initial point with respect to the target H p = discrete prediction horizon i T , j T , k T = target frame unit vectors L = distance to target along target line Q, R = positive semi-definite error and control penalty matrices r = parafoil yaw rate R = final turn radius t 0 = time final turn begins t 1 = time final approach begins t 2 = time of predicted impact T app , des app T = final approach time and desired final approach time t pre = final turn advance timing U = discrete optimal control vector h V , v V = parafoil horizontal and vertical speed W x , W y = target frame wind speed components x, y, z = parafoil inertial positions in the target frame x = parafoil model state vector δ a = parafoil asymmetric brake deflection ∆ = predictive controller discrete sampling period

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Section: B General Purpose Gpu Based Guidance Solutionmentioning

confidence: 99%

Section: B General Purpose Gpu Based Guidance Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Parafoil Terminal Guidance Using Massively Parallel Processing

Rogers

Slegers

2012

AIAA Atmospheric Flight Mechanics Conference

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“…The GPU implementation of this dynamic model, used for stochastic impact point prediction, is constructed by propagating one trajectory per GPU kernel as described in Reference [14]. At each stochastic MPC iteration, initial conditions are transferred to GPU global memory and randomization is performed using the CURAND library [19].…”

Section: Impact Point Predictor Equations Of Motionmentioning

confidence: 99%

“…During Monte Carlo trajectory analysis, each prediction can be run in parallel with no serial dependencies or synchronization steps. In Reference [14], the authors showed that runtime requirements for Monte Carlo analysis could be reduced by 1-2 orders of magnitude when implemented on a graphics processing unit (GPU), leading to the ability to run such simulations in real-time. In the stochastic MPC algorithm developed here, it is proposed that PDF prediction be accomplished through real-time Monte Carlo simulation on embedded GPU hardware, which is rapidly becoming available for low-power autonomous systems applications [15].…”

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confidence: 99%

GPU-enabled projectile guidance for impact area constraints

Rogers

2013

SPIE Proceedings

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Guided projectile engagement scenarios often involve impact area constraints, in which it may be less desirable to incur miss distance on one side of a target or within a specified boundary near the target area. Current projectile guidance schemes such as impact point predictors cannot handle these constraints within the guidance loop, and may produce dispersion patterns that are insensitive to these constraints. In this paper, a new projectile guidance law is proposed that leverages real-time Monte Carlo impact point prediction to continually evaluate the probability of violating impact area constraints. The desired aim point is then adjusted accordingly. Real-time Monte Carlo simulation is enabled within the feedback loop through use of graphics processing units (GPU's), which provide parallel pipelines through which a dispersion pattern can routinely be predicted. The result is a guidance law that can achieve minimum miss distance while avoiding impact area constraints. The new guidance law is described and formulated as a nonlinear optimization problem which is solved in real-time through massively-parallel Monte Carlo simulation. An example simulation is shown in which impact area constraints are enforced and the methodology of stochastic guidance is demonstrated. Finally, Monte Carlo simulations are shown which demonstrate the ability of the stochastic guidance scheme to avoid an arbitrary set of impact area constraints, generating an impact probability density function that optimally trades miss distance within the restricted impact area. The proposed guidance scheme has applications beyond smart weapons to include missiles, UAV's, and other autonomous systems.

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Monte Carlo Simulation of Dynamic Systems on GPU’s

Rogers

2014

Numerical Computations With GPUs

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Projectile Monte-Carlo Trajectory Analysis Using a Graphics Processing Unit

Cited by 17 publications

References 4 publications

Robust Parafoil Terminal Guidance Using Massively Parallel Processing

Robust Parafoil Terminal Guidance Using Massively Parallel Processing

GPU-enabled projectile guidance for impact area constraints

Monte Carlo Simulation of Dynamic Systems on GPU’s

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