Robot Area Coverage Path Planning in Aquatic Environments

This thesis is motivated by real world problems faced in aquatic environments. It addresses the problem of area coverage path planning with robots - the problem of moving an end-effector of a robot over all available space while avoiding existing obstacles. The problem is considered first in a 2D space with a single robot for specific environmental monitoring operations, and then with multi-robot systems — a known NP-complete problem. Next we tackle the coverage problem in 3D space - a step towards underwater mapping of shipwrecks or monitoring of coral reefs. The first part of this thesis leverages human expertise in river exploration and data collection strategies to automate and optimize environmental monitoring and surveying operations using autonomous surface vehicles (ASVs). In particular, four deterministic algorithms for both partial and complete coverage of a river segment are proposed, providing varying path length, coverage density, and turning patterns. These strategies result in increases in accuracy and efficiency compared to manual approaches taken by scientists. The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the ASVs were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in a total of more than 35km of coverage trajectories in the field. In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. The second part of this thesis focuses on environmental monitoring of aquatic domains using a team of Autonomous Surface Vehicles (ASVs) that have Dubins vehicle constraints. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NPcomplete problems. As such, we present two heuristic methods based on a variant of the traveling salesman problem—k-TSP—formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations and with a team of ASVs operating on a 40 000m2 lake area to assess their ability to scale to the real world. Finally, in the third part, a step towards solving the coverage path planning problem in a 3D environment for surveying underwater structures, employing vision-only navigation strategies, is presented. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this work is a step towards enabling the autonomous operation of lightweight agile robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks (CNNs) to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation, with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed

Cooperative Swarm Optimisation of Unmanned Surface Vehicles

Edited version embargoed 10 07.01.2020 Full version: Access restricted permanently due to 3rd party copyright restrictions. Restriction set on 11/04/2019 by AS, Doctoral CollegeWith growing advances in technology and everyday dependence on oceans for resources, the role of unmanned surface vehicles (USVs) has increased many fold. Extensive operations of USVs having naval, civil and scientific applications are currently being undertaken in various complex marine environments and demands are being placed on them to increase their autonomy and adaptability. A key requirement for the autonomous operation of USVs is to possess a multi-vehicle framework where they can operate as a fleet of vehicles in a practical marine environment with multiple advantages such as surveying of wider areas in less time. From the literature, it is evident that a huge number of studies has been conducted in the area of single USV path planning, guidance and control whilst very few studies have been conducted to understand the implications of the multi vehicle approaches to USVs. This present PhD thesis integrates the modules of efficient optimal path planning, robust path following guidance and cooperative swarm aggregation approach towards development of a new hybrid framework for cooperative navigation of swarm of USVs to enable optimal and autonomous operation in a maritime environment. Initially, an effective and novel optimal path planning approach based on the A* algorithm has been designed taking into account the constraint of a safety distance from the obstacles to avoid the collisions in scenarios of moving obstacles and sea surface currents. This approach is then integrated with a novel virtual target path following guidance module developed for USVs where the reference trajectory from the path planner is fed into the guidance system. The novelty of the current work relies on combining the above mentioned integrated path following guidance system with decentralised swarm aggregation behaviour by means of simple potential based attraction and repulsion functions to maintain the centroid of the swarm of USVs and thereby guiding the swarm of USVs onto a reference path. Finally, an optimal and hybrid framework for cooperative navigation and guidance of fleet of USVs, implementable in practical maritime environments and effective for practical applications at sea is presented.Commonwealth Scholarship Commissio