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In this chapter, weRobustMotionPlanning focus on a risk-constrainedOptimal control optimalOptimal control problemOptimal Control Problem (OCP) and explore a model predictive control scheme with distributionally robustRobust riskRisk constraints as a solutionSolution strategy. The primary objective of such a control problemOptimal Control Problem (OCP) is to encode a motionMotion planningPlanning and control taskTask for an autonomous agent. Considering conditional value-at-riskRisk as the riskRisk metric with ambiguity sets based on the WassersteinWasserstein metric and total variation distance, we present various reformulations of the distributionally robustRobust constraints, where the constraintConstraint function encodesCollision avoidance a collisionCollision avoidanceAvoidance condition. We comment on the computational effort in deriving controllers using our schemes and prescribe various approximations.

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Distributionally Robust and Risk-Averse Model Predictive Control for Motion Planning and Control: Reformulations and Computational Issues

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  • 发表日期：

    2025年

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  • Ashish Cherukuri,
  • Anushri Dixit,
  • Astghik Hakobyan

摘要

In this chapter, weRobustMotionPlanning focus on a risk-constrainedOptimal control optimalOptimal control problemOptimal Control Problem (OCP) and explore a model predictive control scheme with distributionally robustRobust riskRisk constraints as a solutionSolution strategy. The primary objective of such a control problemOptimal Control Problem (OCP) is to encode a motionMotion planningPlanning and control taskTask for an autonomous agent. Considering conditional value-at-riskRisk as the riskRisk metric with ambiguity sets based on the WassersteinWasserstein metric and total variation distance, we present various reformulations of the distributionally robustRobust constraints, where the constraintConstraint function encodesCollision avoidance a collisionCollision avoidanceAvoidance condition. We comment on the computational effort in deriving controllers using our schemes and prescribe various approximations.