Autonomous Vehicle Trajectory Planning under Uncertainty Using Stochastic Collocation

Liu, Hong Fu; Zhang, Yu; Chen, Shao Fei; Chen, Jing

doi:10.4028/www.scientific.net/amr.580.175

Cited by 7 publications

(2 citation statements)

References 9 publications

(13 reference statements)

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“…Based on the literature review of collision avoidance formulations, algorithms that are similar to the work in this paper represented the ownship dynamics using a 3 DOF nonlinear point mass model such as the one used by Raghunathan et al 29 and Liu et al 30 Due to the large separation distances (up to 5 nautical miles and 2, 000 feet vertical) required in the NAS, a 3 DOF point mass models provides the right balance between performance and computational complexity when compared to a higher dimensional 6 DOF model. 23 Thus, equations (14) -(18) 23 are the equations of motion used in this research to generate the optimal collision avoidance trajectories.ẋ…”

Section: A Ownship Modelmentioning

confidence: 99%

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

Smith¹

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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The post-911 environment has punctuated the force-multiplying capabilities that remotely piloted aircraft (RPA) provide combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems (UAS) made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to "provide for the safe integration of civil unmanned aircraft systems into the national airspace system as soon as practicable, but not later than September 30, 2015." However, a necessary entrance criterion to operate RPAs in the national airspace system is the ability to Sense and Avoid (SAA) both cooperative and non-cooperative air traffic and attain the same target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, determine best techniques for calculating optimal avoidance trajectories, and second, determine best techniques for estimating an intruder aircraft's trajectory in a stochastic environment. This paper describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for deterministic and stochastic collision avoidance scenarios.

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Section: A Ownship Modelmentioning

confidence: 99%

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

Smith¹

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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“…Further from this, the state estimate shall be used to optimize and control the dynamical system, where the optimal control policy is drawn apparently [1] [2] [3] [4] [5]. From literatures, the applications of the nonlinear stochastic optimal control are widely studied, see for examples, vehicle trajectory planning [6], portfolio selection problem [7], building structural system [8], investment in insurance [9], switching system [10], machine maintenance problem [11], nonlinear differential game problem [12], and viscoelastic systems [13].…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Nonlinear Stochastic Optimal Control Problem Based on Stochastic Approximation Approach

Kek¹,

Sim²,

Leong³

et al. 2018

APM

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In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.

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