Optimization-based Planning and Control for Autonomous Surface Vehicles

Abstract

With autonomy offering a number of benefits in robotics applications, such as increased safety, better consistency and reliability, reduced environmental impact and higher efficiency, it is not surprising that the topic has seen an increase in interest from both the research community as well as commercial and defence industries. In the maritime sector, autonomy has mostly been limited to autonomous underwater vehicles (AUVs), where the operational conditions allow for only limited or delayed communication, making direct or remote control by humans difficult. In recent years however, the focus has shifted to include autonomous surface vehicles (ASVs), with applications such as surveying and mapping, surveillance, and transportation. In order to deliver on the promises of autonomy for ASVs, one of the challenges that needs to be overcome, is designing robust, efficient and safe control systems, enabling the ASVs to plan their mission, make decisions based on sensory feedback, and command the vehicle control surfaces. This thesis presents topics related to optimization and control of ASVs. This includes low-level motion control, mid-level local trajectory planning and collision avoidance (COLAV), and high-level global trajectory planning. The main part of the thesis, is a collection of peer-reviewed articles, six journal articles and three conference papers. In addition to the article collection, the initial part of the thesis contains an introduction to the main topics of low-level motion control, mid-level local trajectory planning and COLAV and high-level global trajectory planning. This provides context to the publications, and explains the relationship between the different publications. In the context of performing autonomous marine operations, one of the first tasks, is to plan a high-level path or trajectory in order to meet the mission objective. This should be done in a way that accounts for geographical data as well as the limitations of the ASV, in order to ensure that the vessel is able to follow the plan without having to worry about colliding into known static obstacles. As part of this thesis, we present three papers concerned with planning high-level global trajectories, which in addition to planning collision free trajectories for ASVs, also finds a trajectory which optimizes a performance measure, such as energy, time and distance. The proposed planning methods combine classical combinatorial planning algorithms and convex optimization into a new class of hybrid methods, which improves both the performance of the algorithms and the optimality of the planned trajectory. Once an ASV is following the high-level global trajectory new obstacles such as other moving vessels and unmapped landmasses may be detected, leaving the initial global trajectory no longer feasible. To solve this problem, a mid-level local trajectory planner is needed, in order re-plan parts of the trajectory such that collisions with the obstacles is avoided. As part of this thesis, we present four papers concerned with planning mid-level local trajectories. Three of these papers focus on the problem of docking and berthing in confined waters, in a way that accounts for the vessel geometry, the harbor layout, and unmapped obstacles from exteroceptive sensors. The fourth paper discusses the problem of risk assessment and COLAV during transit, and proposes a novel approach for representing dynamic obstacles with both measurement and behavioural uncertainty. Once a trajectory has been planned, we would like to execute the plan by maneuvering the ASV. This process, called motion control, involves controlling the actuators and control surfaces of the vessel in a way that follows a course, path or trajectory. For marine vessels, motion control is complicated by the unpredictable nature of the marine environment, and the complex hydrodynamic interactions, which can very significantly during operations. As part of this thesis, we present two papers on reinforcement learning (RL)-based motion control for marine vessels, which demonstrate how on-line learning can be used to optimize the performance of the motion control system.