Underactuated leader-follower synchronisation for multi-agent systems with rejection of unknown disturbances

Author preprintIn this paper leader-follower synchronization is considered for underactuated followers in an inhomogeneous multi-agent system. The goal is to synchronise the motion of a leader and an underactuated follower. Measurements of the leader's position and velocity are available, while the dynamics and trajectory of the leader is unknown. The leader velocities are used as input for a constant bearing guidance algorithm to assure that the follower synchronises its motion to the leader. It is also shown that the proposed leader-follower scheme can be applied to multi-agent systems that are subjected to unknown environmental disturbances. Furthermore, the trajectory of the leader does not need to be known. The stability properties of the complete control scheme and the unactuated internal dynamics are analysed using nonlinear cascaded system theory. Simulation results are presented to validate the proposed control strategy.Preprint version. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning

Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK

Drilling through the over-pressured formations on Skarv field

Master's thesis in Petroleum engineeringThe main objective of this thesis was an investigation and of drilling operations in over-pressured formations on the J-4H/HT2 well on the Skarv field off the coast of mid-Norway. Abnormally high formation pressures on this well were the direct cause of a stuck pipe incident during drilling followed by consequences for the entire well construction process. Based on daily drilling reports the paper presents the sequence of events leading to the stuck pipe incident on well J-4H. It establishes a link between conditions in abnormally pressured zones and the causes of slow drilling process, as well as of the wellbore collapsing around the drill string. The ultimate goal was to understand the situation and suggest potential countermeasures. To achieve this, a wellbore stability evaluation was performed to analyze. The relationship between drilling fluids, drilling technology, well integrity and an offset well J-1H were taken into consideration to compare operations in the same environment. Further, looking for the most probable scenario and results, can give the clear picture of missteps which should have been done. Based on the results alternative approaches are discussed and suggestions are made to improve the quality of operations and avoid similar problems in the future. The wellbore stability analysis showed that more attention should be put on bottom hole assembly design and changes in wellbore conditions. The results of daily drilling reports analyses indicated that the most efficient solution for drilling challenges may be to change the setting deeper an intermediate casing and decrease the drilling mud density after drilling the over-pressured formations, to avoid overbalance

Ceramic composition at Chalcolithic Shiqmim, northern Negev desert, Israel: investigating technology and provenance using thin section petrography, instrumental geochemistry and calcareous nannofossils

Technological innovations in ceramic production and other crafts are hallmarks of the Chalcolithic period (4500–3600 BCE) in the southern Levant, but details of manufacturing traditions have not been fully investigated using the range of analytical methods currently available. This paper presents results of a compositional study of 51 sherds of ceramic churns and other pottery types from the Chalcolithic site of Shiqmim in the northern Negev desert. By applying complementary thin section petrography, instrumental geochemistry and calcareous nannofossil analyses, connections between the raw materials, clay paste recipes and vessel forms of the selected ceramic samples are explored and documented. The study indicates that steps in ceramic manufacturing can be related to both technological choices and local geology. Detailed reporting of the resulting data facilitates future comparative ceramic compositional research that is needed as a basis for testable regional syntheses and to better resolve networks of trade/exchange and social group movement

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters

Path planning and collision avoidance for autonomous surface vehicles I: a review

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles

Coordinated Sensor-Based Area Coverage and Cooperative Localization of a Heterogeneous Fleet of Autonomous Surface Vessels (ASVs)

Sensor coverage with fleets of robots is a complex task requiring solutions to localization, communication, navigation and basic sensor coverage. Sensor coverage of large areas is a problem that occurs in a variety of different environments from terrestrial to aerial to aquatic. In this thesis we consider the aquatic version of the problem. Given a known aquatic environment and collection of aquatic surface vehicles with known kinematic and dynamic constraints, how can a fleet of vehicles be deployed to provide sensor coverage of the surface of the body of water? Rather than considering this problem in general, in this work we consider the problem given a specific fleet consisting of one very well equipped robot aided by a number of smaller, less well equipped devices that must operate in close proximity to the main robot. A boustrophedon decomposition algorithm is developed that incorporates the motion, sensing and communication constraints imposed by the autonomous fleet. Solving the coverage problem leads to a localization/communication problem. A critical problem for a group of autonomous vehicles is ensuring that the collection operates within a common reference frame. Here we consider the problem of localizing a heterogenous collection of aquatic surface vessels within a global reference frame. We assume that one vessel -- the mother robot -- has access to global position data of high accuracy, while the other vessels -- the child robots -- utilize limited onboard sensors and sophisticated sensors on board the mother robot to localize themselves. This thesis provides details of the design of the elements of the heterogeneous fleet including the sensors and sensing algorithms along with the communication strategy used to localize all elements of the fleet within a global reference frame. Details of the robot platforms to be used in implementing a solution are also described. Simulation of the approach is used to demonstrate the effectiveness of the algorithm, and the algorithm and its components are evaluated using a fleet of ASVs