Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints

Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distributed approach to coordinate a team of heterogeneous UAVs cooperating efficiently in patrolling missions around irregular areas, with low communication ranges and memory storage requirements. Hence it can be used with small‐scale UAVs with limited and different capabilities. The presented system uses a modular architecture and solves the problem by dividing the area between all the robots according to their capabilities. Each aerial robot performs a decomposition based algorithm to create covering paths and a ’one‐to‐one’ coordination strategy to decide the path segment to patrol. The system is decentralized and fault‐tolerant. It ensures a finite time to share information between all the robots and guarantees convergence to the desired steady state, based on the maximal minimum frequency criteria. A set of simulations with a team of quad‐rotors is used to validate the approach

An efficient distributed area division method for cooperative monitoring applications with multiple uavs

This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal solution, following a frequency-based approach. Based on the “coordination variables” concept and on a strict neighborhood relation to share information (left, right, above and below neighbors), this technique defines a distributed division protocol to determine coherently the size and shape of the sub-area assigned to each UAV. Theoretically, the convergence time of the proposed solution depends linearly on the number of UAVs. Validation results, comparing the proposed approach with other distributed techniques, are provided to evaluate and analyze its performance following a convergence time criterion.European Union’s Horizon 2020 AERIAL-CORE Project Grant 871479CDTI (sPAIN) “Red Cervera” Programme iMOV3D Spanish R&D projec

Task-driven multi-formation control for coordinated UAV/UGV ISR missions

The report describes the development of a theoretical framework for coordination and control of combined teams of UAVs and UGVs for coordinated ISR missions. We consider the mission as a composition of an ordered sequence of subtasks, each to be performed by a different team. We design continuous cooperative controllers that enable each team to perform a given subtask and we develop a discrete strategy for interleaving the action of teams on different subtasks. The overall multi-agent coordination architecture is captured by a hybrid automaton, stability is studied using Lyapunov tools, and performance is evaluated through numerical simulations

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Algorithmic and combinatorial problems on multi-UAV systems

Mathematics has always been a fundamental piece in robotics and, research in robotics has played an important role in the development of mathematics. This thesis is motivated by the growing interest on problems that appear in aerial robotics applications, specifically, on cooperative systems of multiple aerial robots or drones. Most of the research works in multi-robot systems have focused primarily on construction and validation of working systems, rather than more general and formal analysis of problems and solutions. By contrast, this thesis focuses on formally solving problems of aerial multi-robot systems from a discrete and combinatorial optimization perspective. Inspired on problems of this area, the thesis introduces some new theoretical models and problems of interest for mathematicians and computer scientists. The following topics are covered in this thesis: (1) synchronization: design of a coordination strategy to allow periodical communication between the members of a cooperative team while performing a task along fixed trajectories in a scenario with limited communication range, (2) robustness: analysis of the detrimental effects in the performance of a synchronized system when one or more robots fail, (3) stochastic strategies: performance analysis of a synchronized system using drones with stochastic decision making, and (4) task allocation: decentralized coordination to perform periodical task allocation in order to maintain a balanced work load for all members of a team with limited communication range. In the first part of the thesis, we study the synchronization problem giving a theoretical characterization of the solutions and, we present an algorithm to build a synchronized system for a given set of covering trajectories. The second part focuses on the study of the robustness in a synchronized system regarding to two key aspects: covering of the working area and communication between the members of the team. We rigorously study several combinatorial problems to measure how robust a system is to deal with drones failures. Connections of theseproblemswithnumbertheory, graphtheory, circulantgraphsandpolynomial multiplication are shown. The third part is devoted to an analysis of synchronized systems using random aerial robots. This topic is closely related to the random walk theory. It is shown that stochastic strategies increase the robustness of a synchronized system. Finally, this thesis introduces the block sharing strategy to addresstheproblemofmaintainingabalancedtaskallocationamongtherobotsby using periodical communications. A proof on the convergence to an optimal task allocation is given and, a case study for structure construction using a cooperative team of aerial robots is presented. All algorithms developed in this thesis have been implemented and extensive experiments have been conducted to analyze and validate the proposed methods.Las matemáticas siempre han sido una pieza fundamental en el desarrollo de la robótica, así como los problemas de robótica han jugado un importante papel en el desarrollo de las matemáticas. Esta tesis está motivada por el creciente interés en problemas que aparecen en aplicaciones de robótica aérea, específicamente, está enfocada en sistemas cooperativos de múltiples robots aéreos o drones. La mayoría de los trabajos de investigación en sistemas de robots se han centrado en la construcción y validación de arquitecturas desde un enfoque empírico. Por el contrario, esta tesis enfoca el estudio de problemas relacionados con tareas para equipos de robots aéreos desde el punto de vista de la optimización discreta y combinatoria. Inspirada en problemas de este campo, esta memoria plantea nuevos modelos teóricos y problemas de interés para las matemáticas aplicadas y la ciencia computacional. Enestatesisse abordanlostemassiguientes: (1) sincronización: diseñodeuna estrategia de coordinación que permita comunicación periódica entre los miembros de un equipo cooperativo mientras ejecutan una tarea sobre trayectorias fijadas, (2) robustez: análisis del efecto que produce el fallo de los agentes en un sistema sincronizado, (3)estrategias estocásticas: análisisdelfuncionamientodeunsistema sincronizado cuando se utilizan drones con toma de decisiones aleatorias, y (4) asignación de tareas: coordinación no centralizada usando asignación periódica de tareas que permita mantener una carga de trabajo balanceada. En la primera parte, se estudia teóricamente el problema de la sincronización, dando condiciones necesarias y suficientes para la existencia de solución y se presenta un algoritmo que construye un sistema sincronizado para un conjunto fijado de trayectorias de vuelo. La segunda parte de la tesis estudia la robustez de un sistema sincronizado teniendo en cuenta dos aspectos fundamentales: el cubrimiento del terreno y la comunicación entre los miembros del equipo. Se estudian de forma rigurosa problemas combinatorios que surgen cuando se requiere saber cómo de robusto es un sistema con respecto a fallos. Se muestran conexiones con áreas matemáticas como la teoría de números, la teoría de grafos, los grafos circulantes o multiplicación de polinomios. En la tercera parte de la tesis, se estudia la robustez del sistema cuando se introducen decisiones aleatorias de los drones. Se prueba la relación de este problema con la teoría de caminatas aleatorias y se muestra que el uso de estrategias estocásticas supone una mejora de la robustez del sistema sincronizado. Por último, se propone la estrategia de coordinación por bloques para la asignación balanceada de tareas. Se prueba la convergencia del método a una asignación óptima y se realiza un estudio de caso para la construcción de una estructura mediante un equipo cooperativo de drones. Todos los algoritmos desarrollados en esta tesis han sido implementados y se han llevado a cabo diversos experimentosque demuestran la validez de los métodos propuestos

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

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