55 citations · 71 across the 12 of their papers we have counts for
9 papers · 1 filter
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles
Wenqi Cai, Arash B. Kordabad, Hossein N. Esfahani +2
In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is to find an optimal polic…
Reinforcement Learning based on Scenario-tree MPC for ASVs
Arash Bahari Kordabad, Hossein Nejatbakhsh Esfahani, Anastasios M. Lekkas +1
In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC…
Optimal model-based trajectory planning with static polygonal constraints
Andreas B. Martinsen, Anastasios M. Lekkas, Sebastien Gros
The main contribution of this paper is a novel method for planning globally optimal trajectories for dynamical systems subject to polygonal constraints. The proposed method is a hy…
Trajectory Planning and Control for Automatic Docking of ASVs with Full-Scale Experiments
Glenn Bitar, Andreas B. Martinsen, Anastasios M. Lekkas +1
We propose a method for performing automatic docking of a small autonomous surface vehicle (ASV) by interconnecting an optimization-based trajectory planner with a dynamic position…
Combining system identification with reinforcement learning-based MPC
Andreas B. Martinsen, Anastasios M. Lekkas, Sebastien Gros
In this paper we propose and compare methods for combining system identification (SYSID) and reinforcement learning (RL) in the context of data-driven model predictive control (MPC…
Autonomous docking using direct optimal control
Andreas B. Martinsen, Anastasios M. Lekkas, Sebastien Gros
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 s…