Optimal Partitioning of a Surveillance Space for Persistent Coverage Using Multiple Autonomous Unmanned Aerial Vehicles: An Integer Programming Approach

Unmanned aerial vehicles (UAVs) are an essential tool for the battle eld commander in part because they represent an attractive intelligence gathering platform that can quickly identify targets and track movements of individuals within areas of interest. In order to provide meaningful intelligence in near-real time during a mission, it makes sense to operate multiple UAVs with some measure of autonomy to survey the entire area persistently over the mission timeline. This research considers a space where intelligence has identi ed a number of locations and their surroundings that need to be monitored for a period of time. An integer program is formulated and solved to partition this surveillance space into the minimum number of subregions such that these locations fall outside of each partitioned subregion for e cient, persistent surveillance of the locations and their surroundings. Partitioning is followed by a UAV-to-partitioned subspace matching algorithm so that each subregion of the partitioned surveillance space is assigned exactly one UAV. Because the size of the partition is minimized, the number of UAVs used is also minimized

Cooperative area surveillance strategies using multiple unmanned systems

Recently, the U.S. Department of Defense placed the technological development of intelligence, surveillance, and reconnaissance (ISR) tools at the top of its priority list. Area surveillance that takes place in an urban setting is an ISR tool of special interest. Unmanned aerial vehicles (UAVs) are ideal candidates to perform area surveillance because they are inexpensive and they do not require a human pilot to be aboard. Multiple unmanned systems increase the rate of information flow from the target region and maintain up to date information. The purpose of the research described in this dissertation is to develop and test a system that coordinates multiple UAVs on a wide area coverage surveillance mission. The research presented in this document implements a waypoint generator for multiple aerial vehicles that is especially suited for large area surveillance. The system chooses initial locations for the vehicles and generates a set of balanced sub-trees which cover the region of interest (ROI) for the vehicles. The sub-trees are then optimally combined to form a single minimal tree that spans the entire region. The system transforms the tree path into a series of waypoints suitable for the aerial vehicles. The output of the system is a set of waypoints for each vehicle assigned to the coverage task. Results from computer simulation and flight testing are presented.Ph.D.Committee Chair: Dr. George Vachtsevanos; Committee Member: Ayanna Howard; Committee Member: Dr. Thomas Michaels; Committee Member: Eric Johnson; Committee Member: Linda Will

A Decomposition Strategy for Optimal Coverage of an Area of Interest using Heterogeneous Team of UAVs

In this thesis, we study the problem of optimal search and coverage with heterogeneous team of unmanned aerial vehicles (UAVs). The team must complete the coverage of a given region while minimizing the required time and fuel for performing the mission. Since the UAVs have different characteristics one needs to equalize the ratio of the task to the capabilities of each agent to obtain an optimal solution. A multi-objective task assignment framework is developed for finding the best group of agents that by assigning the optimal tasks would carry out the mission with minimum total cost. Once the optimal tasks for UAVs are obtained, the coverage area (map) is partitioned according to the results of the multi-objective task assignment. The strategy is to partition the coverage area into separate regions so that each agent is responsible for performing the surveillance of its particular region. The decentralized power diagram algorithm is used to partition the map according to the results of the task assignment phase. Furthermore, a framework for solving the task assignment problem and the coverage area partitioning problem in parallel is proposed. A criterion for achieving the minimum number of turns in covering a region a with single UAV is studied for choosing the proper path direction for each UAV. This criterion is extended to develop a method for partitioning the coverage area among multiple UAVs that minimizes the number of turns. We determine the "best" team for performing the coverage mission and we find the optimal workload for each agent that is involved in the mission through a multi-objective optimization procedure. The search area is then partitioned into disjoint subregions, and each agent is assigned to a region having an optimum area resulting in the minimum cost for the entire surveillance mission

Distributed approaches for coverage missions with multiple heterogeneous UAVs for coastal areas.

This Thesis focuses on a high-level framework proposal for heterogeneous aerial, fixed wing teams of robots, which operate in complex coastal areas. Recent advances in the computational capabilities of modern processors along with the decrement of small scale aerial platform manufacturing costs, have given researchers the opportunity to propose efficient and low-cost solutions to a wide variety of problems. Regarding marine sciences and more generally coastal or sea operations, the use of aerial robots brings forth a number of advantages, including information redundancy and operator safety. This Thesis initially deals with complex coastal decomposition in relation with a vehicles’ on-board sensor. This decomposition decreases the computational complexity of planning a flight path, while respecting various aerial or ground restrictions. The sensor-based area decomposition also facilitates a team-wide heterogeneous solution for any team of aerial vehicles. Then, it proposes a novel algorithmic approach of partitioning any given complex area, for an arbitrary number of Unmanned Aerial Vehicles (UAV). This partitioning schema, respects the relative flight autonomy capabilities of the robots, providing them a corresponding region of interest. In addition, a set of algorithms is proposed for obtaining coverage waypoint plans for those areas. These algorithms are designed to afford the non-holonomic nature of fixed-wing vehicles and the restrictions their dynamics impose. Moreover, this Thesis also proposes a variation of a well-known path tracking algorithm, in order to further reduce the flight error of waypoint following, by introducing intermediate waypoints and providing an autopilot parametrisation. Finally, a marine studies test case of buoy information extraction is presented, demonstrating in that manner the flexibility and modular nature of the proposed framework.Esta tesis se centra en la propuesta de un marco de alto nivel para equipos heterogéneos de robots de ala fija que operan en áreas costeras complejas. Los avances recientes en las capacidades computacionales de los procesadores modernos, junto con la disminución de los costes de fabricación de plataformas aéreas a pequeña escala, han brindado a los investigadores la oportunidad de proponer soluciones eficientes y de bajo coste para enfrentar un amplio abanico de cuestiones. Con respecto a las ciencias marinas y, en términos más generales, a las operaciones costeras o marítimas, el uso de robots aéreos conlleva una serie de ventajas, incluidas la redundancia de la información y la seguridad del operador. Esta tesis trata inicialmente con la descomposición de áreas costeras complejas en relación con el sensor a bordo de un vehículo. Esta descomposición disminuye la complejidad computacional de la planificación de una trayectoria de vuelo, al tiempo que respeta varias restricciones aéreas o terrestres. La descomposición del área basada en sensores también facilita una solución heterogénea para todo el equipo para cualquier equipo de vehículos aéreos. Luego, propone un novedoso enfoque algorítmico de partición de cualquier área compleja dada, para un número arbitrario de vehículos aéreos no tripulados (UAV). Este esquema de partición respeta las capacidades relativas de autonomía de vuelo de los robots, proporcionándoles una región de interés correspondiente. Además, se propone un conjunto de algoritmos para obtener planes de puntos de cobertura para esas áreas. Estos algoritmos están diseñados teniendo en cuenta la naturaleza no holonómica de los vehículos de ala fija y las restricciones que impone su dinámica. En ese sentido, esta Tesis también ofrece una variación de un algoritmo de seguimiento de rutas bien conocido, con el fin de reducir aún más el error de vuelo del siguiente punto de recorrido, introduciendo puntos intermedios y proporcionando una parametrización del piloto automático. Finalmente, se presenta un caso de prueba de estudios marinos de extracción de información de boyas, que demuestra de esa manera la flexibilidad y el carácter modular del marco propuesto

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Minimizing Turns in Single and Multi Robot Coverage Path Planning

Spurred by declining costs of robotics, automation is becoming a prevalent area of interest for many industries. In some cases, it even makes economic sense to use a team of robots to achieve a goal faster. In this thesis we study sweep coverage path planning, in which a robot or a team of robots must cover all points in a workspace with its footprint. In many coverage applications, including cleaning and monitoring, it is beneficial to use coverage paths with minimal robot turns. In the first part of the thesis, we address this for a single robot by providing an efficient method to compute the minimum altitude of a non-convex polygonal region, which captures the number of parallel line segments, and thus turns, needed to cover the region. Then, given a non-convex polygon, we provide a method to cut the polygon into two pieces that minimizes the sum of their altitudes. Given an initial convex decomposition of a workspace, we apply this method to iteratively re-optimize and delete cuts of the decomposition. Finally, we compute a coverage path of the workspace by placing parallel line segments in each region, and then computing a tour of the segments of minimum cost. We present simulation results on several workspaces with obstacles, which demonstrate improvements in both the number of turns in the final coverage path and runtime. In the second part of the thesis, we extend the concepts developed for a single robot coverage to a multi robot case. We provide a metric χ that approximates the cost of a coverage path, which accounts for the cost of turns. Given a polygon, we provide a method for cutting a polygon into two that would minimize the maximum cost χ between the two polygons. Provided with an initial n-cell decomposition, we apply this method in the iterative manner to re-optimize cuts in order to minimize the maximum cost χ over all cells in the decomposition. For each cell in the re-optimized n-cell decomposition, a single robot coverage path is computed using the minimum altitude decomposition. We present the simulation results that demonstrate improvements in the maximum cost as well as the range of costs over all robots in the team

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Coverage Path Planning for Autonomous Robots

Coverage Path Planning (CPP) is a problem of path computation with minimal length that guarantees to scan the entire area of interest. CPP finds its application in diverse fields like cartography, inspection, precision agriculture, milling, and demining. However, this thesis is a prominent step to solve CPP for real-world problems where environment poses multiple challenges. At first, four significant and pressing challenges for CPP in extreme environment are identified. Each challenge is formulated as a problem and its solution has been presented as a dedicated chapter in this thesis. The first problem, Goal-Oriented Sensor based CPP, focuses on cumbersome tasks like Nuclear Decommissioning, where the robot covers an abandoned site in tandem with the goal to reach a static target in minimal time. To meet the grave speeding-up challenge, a novel offline-online strategy is proposed that efficiently models the site using floor plans and grid maps as a priori information. The proposed strategy outperforms the two baseline approaches with reduction in coverage time by 45%- 82%. The second problem explores CPP of distributed regions, applicable in post-disaster scenarios like Fukushima Daiichi. Experiments are conducted at radiation laboratory to identify the constraints robot would be subjected to. The thesis is successfully able to diagnose transient damage in the robot’s sensor after 3 Gy of gamma radiation exposure. Therefore, a region order travel constraint known as Precedence Provision is imposed for successful coverage. The region order constraint allows the coverage length to be minimised by 65% in comparison to state-of-the-art techniques. The third problem identifies the major bottleneck of limited on-board energy that inhibits complete coverage of distributed regions. The existing approaches allow robots to undertake multiple tours for complete coverage which is impractical in many scenarios. To this end, a novel algorithm is proposed that solves a variant of CPP where the robot aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. The proposed algorithm covers 23% - 35% more area in comparison to the state-of-the-art approaches. Finally, the last problem, an extension of the second and third problems, deals with the problem of CPP over a set of disjoint regions using a fleet of heterogeneous aerial robots. A heuristic is proposed to deliver solutions within acceptable time limits. The experiments demonstrate that the proposed heuristic solution reduces the energy cost by 15-40% in comparison to the state-of-the art solutions

Design of a protocol for event-based network reconfiguration of active vision systems

Projecte final de carrera fet en col.laboració amb Leibniz Universtät HannoverCatalà: Avui en dia la vigilancia de grans àreas, com ara bancs, aeroports o ciutats es basa principalment en sistemas de video. Les Active Cameras (ACs) juguen un paper important per als sistemes de seguretat, ja que combinen video detecció, processament de video i comunicació en un sol dispositiu. Un punt feble és que les ACs son generalment fixes i poden apareixer oclusions que poden crear punts cecs al sistema. Aquests punts cecs poden ser superats mitjançant l?ús de ACs mòbils crean una xarxa mòbil, anomenada Active Camera Network (ACN), presentades en aquesta tesi. No obstant això, la mobilitat de les ACs ve juntament amb desafiaments en termes de coordinació i configuración. A més d?això, el cost de les ACs es més gran en comparació a les xarxes de cameres estatiques, però el nombre de guàrdies necessaris per inspeccionar una gran àrea com una ciutat per exemple o per controlar una gran quantitat de monitors es pot reduir considerablement amb les ACNs. El nostre objectiu és implementar l?arquitectura de un sistema auto-reconfigurable per una xarxa de ACs que poden anar muntades en robots mòbils pel terra o en microvehicles aeris (MAV). Així, les ACs decidirán per si mateixes on actualizar la seva posición per tal d?aconseguir un rendiment òptim del sistema. Per assolir aquest objectiu, les ACs aumentaran o disminuiran les regions espacials redundants amb el seus veïns fent focus en les regions mes sobrecarregades. El protocol presentat en aquesta tesi adapta la posición de les ACs per detectar les diferents trajectories que travessan la zona de vigilancia i que poden evolucionar amb el temps. Les simulacions han demostrat que el protocol presentat augmenta el rendiment general del sistema fins un 190% més gràcies a la reconfiguració i cooperación entre les ACs veïnes.Castellano: Hoy en día la vigilancia de grandes áreas, tales como bancos, aeropuertos o ciudades se basa principalmente en sistemas de video vigilancia. Las Active Cameras (ACs) juegan un papel importante para los sistemas de seguridad, ya que combinan video detección, procesamiento de video y comunicación en un solo dispositivo. Un punto débil es que las ACs son generalmente fijas y pueden aparecer oclusiones que creen puntos ciegos en el sistema. Estos puntos ciegos pueden ser superados mediante el uso de ACs móviles creando una red móvil, llamada Active Camera Network (ACN), presentadas en esta tesis. Sin embargo, la movilidad de las ACs viene junto con desafíos en términos de coordinación y configuración. Además de esto, el coste de las ACs es mayor en comparación a las redes de cámaras estáticas, pero el número de guardias necesarios para inspeccionar una gran área como una ciudad por ejemplo o para controlar una gran cantidad de monitores se puede reducir considerablemente con las ACNs. Nuestro objetivo es implementar la arquitectura de un sistema auto-reconfigurable para una red de ACs que pueden ir montadas en robots móviles por el suelo o en micro vehículos aéreos (MAV). Así, las ACs decidirán por sí mismas donde actualizar sus posiciones con el fin de conseguir un rendimiento óptimo del sistema. Para alcanzar este objetivo, las ACs aumentarán o disminuirán las regiones espaciales redundantes con sus vecinos haciendo foco en las regiones más sobrecargadas. El protocolo presentado en esta tesis adapta la posición de las ACs para detectar las diferentes trayectorias que atraviesan la zona de vigilancia y que pueden evolucionar con el tiempo. Las simulaciones han demostrado que el protocolo presentado aumenta el rendimiento general del sistema hasta un 190% más gracias a la reconfiguración y cooperación entre las ACs vecinas.English: Nowadays surveillance of large areas, such as banks, airports or cities is mostly based on vision systems. Smart Cameras (SCs) play an important role for security systems as they combine video sensing, video processing and communication within a single device. One weak point is that SCs are usually stationary and so occlusions may create blind spots in the system. These blind spots may be overcome by using mobile SCs, so called Active Camera Networks (ACNs), as introduced in this thesis. Nevertheless, mobility of SCs come along with challenges in terms of coordination and configuration. In addition to this, the cost of ACs is higher in comparison to static camera networks but the number of guards needed to survey a large area like a city or to control a lot of monitors can be reduced considerably with AC networks. Our goal is to implement a self-reconfiguration system architecture for networked smart cameras that could be mounted either on mobile robots on the ground or Micro Air Vehicles (MAVs). Thus, the ACs will decide by themselves where to update their position in order to achieve the optimal system's performance. To reach that goal, ACs will increase or decrease spatial redundancy regions with their neighbours to overcome overloaded regions. The protocol presented in this thesis adapts the position of the ACN to the different trajectories that traverse a surveillance area over time. The simulations have shown that the presented protocol increase the overall performance due to the node reconfiguration and cooperation between neighbouring ACs