68 research outputs found

    Optimal path planning of unmanned surface vehicles

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    The publisher has released this paper this under the Creative Commons CC BY-NC-ND licence. This release is used to provide the authority for open access deposit in Pearl, but please note that the licence forbids commercial use and the distribution of derivative works.Present study reviews the current methodologies adopted for optimal path planning of single unmanned surface vehicles and studies associated with swarm of unmanned surface vehicles. This also discusses the challenges and scopes, which can act as objectives, for future research towards path planning of such marine craft

    Reference Model for Interoperability of Autonomous Systems

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    This thesis proposes a reference model to describe the components of an Un-manned Air, Ground, Surface, or Underwater System (UxS), and the use of a single Interoperability Building Block to command, control, and get feedback from such vehicles. The importance and advantages of such a reference model, with a standard nomenclature and taxonomy, is shown. We overview the concepts of interoperability and some efforts to achieve common refer-ence models in other areas. We then present an overview of existing un-manned systems, their history, characteristics, classification, and missions. The concept of Interoperability Building Blocks (IBB) is introduced to describe standards, protocols, data models, and frameworks, and a large set of these are analyzed. A new and powerful reference model for UxS, named RAMP, is proposed, that describes the various components that a UxS may have. It is a hierarchical model with four levels, that describes the vehicle components, the datalink, and the ground segment. The reference model is validated by showing how it can be applied in various projects the author worked on. An example is given on how a single standard was capable of controlling a set of heterogeneous UAVs, USVs, and UGVs

    Autumn 2016 Full Issue

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    Disruptive Technologies with Applications in Airline & Marine and Defense Industries

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    Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp

    Human-machine interaction for unmanned surface systems

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    This research investigated the human-machine interaction (HMI) technologies for human-robot teams operating as unmanned surface systems (USS). An pilot role was found to be the most prevalent in the USS-related literature but additional human roles were determined to likely be necessary (e.g., Mission Specialist} though were not documented; interface needs have not yet been determined for any role. The human interfaces used by 67 Micro and Small X, Intermediate, Harbor, Fleet, and E,F,G-Class platforms were examined and it was determined that: i) the research literature does not well characterize the human roles present in unmanned surface systems, ii) domain complexity may necessitate increased automation of the robot platform for the human team, and iii) that unmanned surface vehicles likely lay on the human-machine interaction spectrum between unmanned ground vehicles and unmanned aerial vehicles. This work is expected to serve as a reference for future design and refinement of human interfaces for USSs and as a foundation for better understanding HMI in USSs

    A survey of formation control and motion planning of multiple unmanned vehicles

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    The increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development

    Model-Predictive Strategy Generation for Multi-Agent Pursuit-Evasion Games

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    Multi-agent pursuit-evasion games can be used to model a variety of different real world problems including surveillance, search-and-rescue, and defense-related scenarios. However, many pursuit-evasion problems are computationally difficult, which can be problematic for domains with complex geometry or large numbers of agents. To compound matters further, practical applications often require planning methods to operate under high levels of uncertainty or meet strict running-time requirements. These challenges strongly suggest that heuristic methods are needed to address pursuit-evasion problems in the real world. In this dissertation I present heuristic planning techniques for three related problem domains: visibility-based pursuit-evasion, target following with differential motion constraints, and distributed asset guarding with unmanned sea-surface vehicles. For these domains, I demonstrate that heuristic techniques based on problem relaxation and model-predictive simulation can be used to efficiently perform low-level control action selection, motion goal selection, and high-level task allocation. In particular, I introduce a polynomial-time algorithm for control action selection in visibility-based pursuit-evasion games, where a team of pursuers must minimize uncertainty about the location of an evader. The algorithm uses problem relaxation to estimate future states of the game. I also show how to incorporate a probabilistic opponent model learned from interaction traces of prior games into the algorithm. I verify experimentally that by performing Monte Carlo sampling over the learned model to estimate the location of the evader, the algorithm performs better than existing planning approaches based on worst-case analysis. Next, I introduce an algorithm for motion goal selection in pursuit-evasion scenarios with unmanned boats. I show how a probabilistic model accounting for differential motion constraints can be used to project the future positions of the target boat. Motion goals for the pursuer boat can then be selected based on those projections. I verify experimentally that motion goals selected with this technique are better optimized for travel time and proximity to the target boat when compared to motion goals selected based on the current position of the target boat. Finally, I introduce a task-allocation technique for a team of unmanned sea-surface vehicles (USVs) responsible for guarding a high-valued asset. The team of USVs must intercept and block a set of hostile intruder boats before they reach the asset. The algorithm uses model-predictive simulation to estimate the value of high-level task assignments, which are then realized by a set of learned low-level behaviors. I show experimentally that using model-predictive simulations based on Monte-Carlo sampling is more effective than hand-coded evaluation heuristics

    Energy efficient path planning and model checking for long endurance unmanned surface vehicles.

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    In this dissertation, path following, path planning, collision avoidance and model checking algorithms were developed and simulated for improving the level of autonomy for Unmanned Surface Vehicle (USV). Firstly, four path following algorithms, namely, Carrot Chasing, Nonlinear Guidance Law, Pure pursuit and LOS, and Vector Field algorithms, were compared in simulation and Carrot Chasing was tested in Unmanned Safety Marine Operations Over The Horizon (USMOOTH) project. Secondly, three path planning algorithms, including Voronoi-Visibility shortest path planning, Voronoi-Visibility energy efficient path planning and Genetic Algorithm based energy efficient path planning algorithms, are presented. Voronoi-Visibility shortest path planning algorithm was proposed by integrating Voronoi diagram, Dijkstra’s algorithm and Visibility graph. The path quality and computational efficiency were demonstrated through comparing with Voronoi algorithms. Moreover, the proposed algorithm ensured USV safety by keeping the USV at a configurable clearance distance from the coastlines. Voronoi-Visibility energy efficient path planning algorithm was proposed by taking sea current data into account. To address the problem of time-varying sea current, Genetic Algorithm was integrated with Voronoi-Visibility energy efficient path planning algorithm. The energy efficiency of Voronoi-Visibility and Genetic Algorithm based algorithms were demonstrated in simulated missions. Moreover, collision avoidance algorithm was proposed and validated in single and multiple intruders scenarios. Finally, the feasibility of using model checking for USV decision-making systems verification was demonstrated in three USV mission scenarios. In the final scenario, a multi-agent system, including two USVs, an Unmanned Aerial Vehicle (UAV), a Ground Control Station (GCS) and a wireless mesh network, were modelled using Kripke modelling algorithm. The modelled uncertainties include communication loss, collision risk, fault event and energy states. Three desirable properties, including safety, maximum endurance, and fault tolerance, were expressed using Computational Tree Logic (CTL), which were verified using Model Checker for Multi-Agent System (MCMAS). The verification results were used to retrospect and improve the design of the decision-making system.PhD in Aerospac

    An adaptive autopilot design for an uninhabited surface vehicle

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    An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council
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