Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

Lew, Thomas; Bonalli, Riccardo; Pavone, Marco

doi:10.23919/ecc51009.2020.9143595

Cited by 39 publications

(50 citation statements)

References 81 publications

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“…Future work may consider extending this analysis to tackle more general problem formulations, e.g., risk measures as costs, and state (chance) constraints. In this context, some preliminary results using SCP only exist for discrete time problem formulations [24]. However, tackling continuous time formulations will require more sophisticated necessary conditions for optimality.…”

Section: Discussionmentioning

confidence: 99%

Sequential Convex Programming For Non-Linear Stochastic Optimal Control

Bonalli¹,

Lew²,

Pavone³

2020

Preprint

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We introduce a sequential convex programming framework to solve general nonlinear stochastic optimal control problems in finite dimension, where uncertainties are modeled by a multidimensional Wiener process. We provide sufficient conditions for the convergence of the method. Moreover, we prove that, when convergence is achieved, sequential convex programming finds a candidate locally optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. We leverage those properties to design a practical numerical method to solve non-linear stochastic optimal control problems that is based on a deterministic transcription of stochastic sequential convex programming.

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Section: Discussionmentioning

confidence: 99%

Sequential Convex Programming For Non-Linear Stochastic Optimal Control

Bonalli¹,

Lew²,

Pavone³

2020

Preprint

Self Cite

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“…In particular, we take an MPC approach known as Sequential Quadratic Programming (SQP), which iteratively solves locally quadratic sub-problems to converge to a globally (more) optimal solution [7]. Particularly in the robotics domain, this approach is well-suited due to its reduced computational costs and flexibility for handling a wide variety of costs and constraints [35,25]. A common criticism of SQP-based MPC (and nonlinear MPC methods in general) is that they can suffer from being susceptible to local minima.…”

Section: Related Workmentioning

confidence: 99%

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

Fan¹,

Otsu²,

Kubo

et al. 2021

Robotics: Science and Systems XVII

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Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertaintyaware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube.

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“…Providing safety guarantees for learning-based control techniques has received significant attention recently [39]- [46]. In particular, optimization-based control synthesis with CLF and CBF constraints has been considered for systems subject to additive stochastic disturbances [47]- [49].…”

Section: Related Workmentioning

confidence: 99%

Control Barriers in Bayesian Learning of System Dynamics

Dhiman,

Khojasteh,

Franceschetti

et al. 2020

Preprint

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This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distribution over the system dynamics. Specifically, we use a matrix variate Gaussian process (MVGP) regression approach with efficient covariance factorization to learn the drift and input gain terms of a nonlinear control-affine system. The MVGP distribution is then used to optimize the system behavior and ensure safety with high probability, by specifying control Lyapunov function (CLF) and control barrier function (CBF) chance constraints. We show that a safe control policy can be synthesized for systems with arbitrary relative degree and probabilistic CLF-CBF constraints by solving a second order cone program (SOCP). Finally, we extend our design to a self-triggering formulation, adaptively determining the time at which a new control input needs to be applied in order to guarantee safety.

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Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

Cited by 39 publications

References 81 publications

Sequential Convex Programming For Non-Linear Stochastic Optimal Control

Sequential Convex Programming For Non-Linear Stochastic Optimal Control

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

Control Barriers in Bayesian Learning of System Dynamics

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