arXiv subjects

Sebastien Gros

Publications and source records attributed to Sebastien Gros.

58 records · Page 4Linked to original sources

arXiv2019

Autonomous docking using direct optimal control

We propose a method for performing autonomous docking of marine vessels using numerical optimal control. The task is framed as a dynamic positioning problem, with the addition of spatial constraints that ensure collision avoidance. The proposed method is an all-encompassing procedure for performing both docking, maneuvering, dynamic positioning and control allocation. In addition, we show that the method can be implemented as a real-time MPC-based algorithm on simulation results of a supply vessel.

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arXiv2019

Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part II - Deterministic Case

In this paper, we present a methodology to deploy the deterministic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametric optimization problem, where safety is enforced via hard constraints. For continuous input space, imposing safety restrictions on the exploration needed to deploying the deterministic policy gradient method poses some technical difficulties, which we address here. We will investigate in particular policy approximations based on robust Nonlinear Model Predictive Control (NMPC), where safety can be treated explicitly. For the sake of brevity, we will detail the construction of the safe scheme in the robust linear MPC context only. The extension to the nonlinear case is possible but more complex. We will additionally present a technique to maintain the system safety throughout the learning process in the context of robust linear MPC. This paper has a companion paper treating the stochastic policy gradient case.

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arXiv2019

Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part I - Stochastic case

We present a methodology to deploy the stochastic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametric optimization problem, allowing one to enforce safety via hard constraints. For continuous input spaces, imposing safety restrictions on the stochastic policy can make the sampling and evaluation of its density difficult. This paper proposes a computationally effective approach to solve that issue. We will focus on policy approximations based on robust Nonlinear Model Predictive Control (NMPC), where safety can be treated explicitly. For the sake of brevity, we will detail safe policies in the robust linear MPC context only. The extension to the nonlinear case is possible but more complex. We will additionally present a technique to maintain the system safety throughout the learning process in the context of robust linear MPC. This paper has a companion paper treating the deterministic policy gradient case.

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arXiv2017

A Feasibility-Enforcing Primal-Decomposition SQP Algorithm for Optimal Vehicle Coordination

In this paper we consider the problem of coordinating autonomous vehicles approaching an intersection. We cast the problem in the distributed optimisation framework and propose an algorithm to solve it in real time. We extend previous work on the topic by testing two alternative algorithmic solutions in simulations. Moreover, we test our algorithm in experiments using real cars on a test track. The experimental results demonstrate the applicability and real-time feasibility of the algorithm.

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