Path and trajectory planning of a tethered UAV-UGV marsupial robotics system

This paper addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether that has a controllable length. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly exploring Random Trees (RRT*) that takes into account constraints related to the positions of UAV, UGV, tether and the 3D environment. The specialization of the main RRT* methods allows us to obtain feasible solutions in short times. Then, the paper presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion that impose limits on the velocities and accelerations of the robots. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV, UGV, and tether.Comment: 8 pages, 4 figures, 2 table

Monitoring using Heterogeneous Autonomous Agents.

This dissertation studies problems involving different types of autonomous agents observing objects of interests in an area. Three types of agents are considered: mobile agents, stationary agents, and marsupial agents, i.e., agents capable of deploying other agents or being deployed themselves. Objects can be mobile or stationary. The problem of a mobile agent without fuel constraints revisiting stationary objects is formulated. Visits to objects are dictated by revisit deadlines, i.e., the maximum time that can elapse between two visits to the same object. The problem is shown to be NP-complete and heuristics are provided to generate paths for the agent. Almost periodic paths are proven to exist. The efficacy of the heuristics is shown through simulation. A variant of the problem where the agent has a finite fuel capacity and purchases fuel is treated. Almost periodic solutions to this problem are also shown to exist and an algorithm to compute the minimal cost path is provided. A problem where mobile and stationary agents cooperate to track a mobile object is formulated, shown to be NP-hard, and a heuristic is given to compute paths for the mobile agents. Optimal configurations for the stationary agents are then studied. Several methods are provided to optimally place the stationary agents; these methods are the maximization of Fisher information, the minimization of the probability of misclassification, and the minimization of the penalty incurred by the placement. A method to compute optimal revisit deadlines for the stationary agents is given. The placement methods are compared and their effectiveness shown using numerical results. The problem of two marsupial agents, one carrier and one passenger, performing a general monitoring task using a constrained optimization formulation is stated. Necessary conditions for optimal paths are provided for cases accounting for constrained release of the passenger, termination conditions for the task, as well as retrieval and constrained retrieval of the passenger. A problem involving two marsupial agents collecting information about a stationary object while avoiding detection is then formulated. Necessary conditions for optimal paths are provided and rectilinear motion is demonstrated to be optimal for both agents.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111439/1/jfargeas_1.pd

Three-dimensional localization and mapping for mobile robot in disaster environments

To relieve damages of earthquake disaster, &#34;The Special Project for Earthquake Disaster Mitigation in Urban Areas&#34; have been kicked off in Japan. Our research group is a part of the sub-project &#34;modeling of disaster environment for search and rescue&#34; since 2002. In this project, our group aims to develop a three-dimensional mapping's algorithm that is installed in a mobile robot to search victims in a collapsed building. To realize this mission, it is important to map environment information, and also the mapping requires localization simultaneously. (This is called &#34;SLAM problem&#34;.) In this research, we use three-dimensional map by laser range finder, and we also estimate its location in a global map using correlation technique. In this paper, we introduce our localization and mapping method, and we report a result of preparatory experiment for localization. </p

Marsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이범희.This dissertation presents time-efficient approaches to path planning for initial deployment and collection of a heterogeneous marsupial robot team consists of a large-scale carrier robot and multiple mobile robots. Although much research has been conducted about task allocation and path planning of multi-robot systems, the path planning problems for deployment and collection of a marsupial robot team have not been fully addressed. The objectives of the problems are to minimize the duration that mobile robots require to reach their assigned task locations and return from those locations. Taking the small mobile robot's energy constraint into account, a large-scale carrier robot, which is faster than a mobile robot, is considered for efficient deployment and collection. The carrier robot oversees transporting, deploying, and retrieving of mobile robots, whereas the mobile robots are responsible for carrying out given tasks such as reconnaissance and search and rescue. The path planning methods are introduced in both an open space without obstacles and a roadmap graph which avoids obstacles. For the both cases, determining optimal path requires enormous search space whose computational complexity is equivalent to solving a combinatorial optimization problem. To reduce the amount of computation, both task locations and mobile robots to be collected are divided into a number of clusters according to their geographical adjacency and their energies. Next, the cluster are sorted based on the location of the carrier robot. Then, an efficient path for the carrier robot can be generated by traveling to each deploying and loading location relevant to each cluster. The feasibility of the proposed algorithms is demonstrated through several simulations in various environments including three-dimensional space and dynamic task environment. Finally, the performance of the proposed algorithms is also demonstrated by comparing with other simple methods.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Multi-robot system 1 1.1.2 Marsupial robot team 3 1.2 Contributions of the thesis 9 Chapter 2 Related Work 13 2.1 Multi-robot path planning 14 2.2 Multi-robot exploration 14 2.3 Multi-robot task allocation 15 2.4 Simultaneous localization and mapping 15 2.5 Motion planning of collective swarm 16 2.6 Marsupial robot team 18 2.6.1 Multi-robot deployment 18 2.6.2 Marsupial robot 19 2.7 Robot collection 23 2.8 Roadmap generation 24 2.8.1 Geometric algorithms 24 2.8.2 Sampling-based algorithms 25 2.9 Novelty of the thesis 26 Chapter 3 Preliminaries 27 3.1 Notation 27 3.2 Assumptions 29 3.3 Clustering algorithm 30 3.4 Minimum bounded circle and sphere of a cluster 32 Chapter 4 Deployment of a Marsupial Robot Team 35 4.1 Problem definition 35 4.2 Complexity analysis 37 4.3 Optimal deployment path planning for two tasks 38 4.3.1 Deployment for two tasks in 2D space 39 4.3.2 Deployment for two tasks in 3D space 41 4.4 Path planning algorithm of a marsupial robot team for deployment 42 4.5 Simulation result 49 4.5.1 Simulation setup 49 4.5.2 Deployment scenarios in 2D space 50 4.5.3 Deployment scenarios in 3D space 57 4.5.4 Deployment in a dynamic environment 60 4.6 Performance evaluation 62 4.6.1 Computation time 62 4.6.2 Efficiency of the path 64 Chapter 5 Collection of a Marsupial Robot Team 67 5.1 Problem definition 68 5.2 Complexity analysis 70 5.3 Optimal collection path planning for two rovers 71 5.3.1 Collection for two rovers in 2D space 71 5.3.2 Collection for two rovers in 3D space 75 5.4 Path planning algorithm of a marsupial robot team for collection 76 5.5 Simulation result 83 5.5.1 Collection scenarios in 2D space 83 5.5.2 Collection scenarios in 3D space 88 5.5.3 Collection in a dynamic environment 91 5.6 Performance evaluation 93 5.6.1 Computation time 93 5.6.2 Efficiency of the path 95 Chapter 6 Deployment of a Marsupial Robot Team using a Graph 99 6.1 Problem definition 99 6.2 Framework 101 6.3 Probabilistic roadmap generation 102 6.3.1 Global PRM 103 6.3.2 Local PRM 105 6.4 Path planning strategy 105 6.4.1 Clustering scheme 106 6.4.2 Determining deployment locations 109 6.4.3 Path smoothing 113 6.4.4 Path planning algorithm for a marsupial robot team 115 6.5 Simulation result 116 6.5.1 Outdoor space without obstacle 116 6.5.2 Outdoor space with obstacles 118 6.5.3 Office area 119 6.5.4 University research building 122 Chapter 7 Conclusion 125 Bibliography 129 초록 151Docto

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201

Deployment Planning for Multiple Marsupial Robots

Multi-robot systems are versatile and extremely capable of exploration tasks in complex environments. Increasingly sophisticated planners, which incorporate new features of a multi-robot system, are necessary for the operation of the systems. Marsupial robots are multi-robot systems consisting of a carrier robot (e.g., a ground vehicle), which is highly capable and has a long mission duration, and at least one passenger robot (e.g., a short-duration aerial vehicle) transported by the carrier. These heterogeneous robot systems are more flexible than traditional homogeneous multi-robot systems in handling the constraints of a complex environment. However, the physical coupling of a carrier robot and passenger robots requires planners that are capable of accounting for passenger robot deployment planning in exploration. In this thesis, we present two algorithms for deploying passenger robots from marsupial robot systems, culminating into a generalized deployment framework. First, we address the single marsupial robot case: deploying multiple passenger robots from a single carrier robot. We optimize the performance of passenger robot deployment by using an algorithm that reasons over uncertainty by exploiting information about the prior probability distribution of features of interest in the environment. Our algorithm is formulated as a solution to a sequential stochastic assignment problem (SSAP). The key feature of the algorithm is a recurrence relationship that defines a set of observation thresholds that are used to decide when to deploy passenger robots. Our algorithm computes the optimal policy in O(NR) time, where N is the number of deployment decision points and R is the number of passenger robots to be deployed. We conducted drone deployment exploration experiments on real-world data from the DARPA Subterranean challenge to test the SSAP algorithm. Our results show that our deployment algorithm outperforms other competing algorithms, such as the classic secretary approach and baseline partitioning methods, and is comparable to an offline oracle algorithm. Secondly, we expand to the multiple marsupial robot case, deploying multiple passenger robots from multiple carrier robots. While the single marsupial robot deployment algorithm is an optimal, polynomial-time solution, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo tree search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms

Scale estimation by a robot in an urban search and rescue environment

Urban Search and Rescue (USAR) involves having to enter and explore partially collapsed buildings in search for victims trapped by the collapse. There are many hazards in doing this, because of the possibility of additional collapses, explosions, fires, or flooding of the area being searched. The use of robots for USAR would increase the safety of the operation for the humans involved, and make the operation faster, because the robots could penetrate areas inaccessible to human beings. Teleoperated robots have been deployed in USAR situations to explore confined spaces in the collapsed buildings and send back images of the interior to rescuers. These deployments have resulted in the identification of several problems found during the operation of these robots. This thesis addresses a problem that has been encountered repeatedly in these robots: the determination of the scale of unrecognizable objects in the camera views from the robot. A procedure that would allow the extraction of size using a laser pointer mounted on the robot's camera is described, and an experimental setup and results that verify this procedure have been shown. Finally, ways to extend the procedure have been explore

Synthesis of formation control for an aquatic swarm robotics system

Formations are the spatial organization of objects or entities according to some predefined pattern. They can be found in nature, in social animals such as fish schools, and insect colonies, where the spontaneous organization into emergent structures takes place. Formations have a multitude of applications such as in military and law enforcement scenarios, where they are used to increase operational performance. The concept is even present in collective sports modalities such as football, which use formations as a strategy to increase teams efficiency. Swarm robotics is an approach for the study of multi-robot systems composed of a large number of simple units, inspired in self-organization in animal societies. These have the potential to conduct tasks too demanding for a single robot operating alone. When applied to the coordination of such type of systems, formations allow for a coordinated motion and enable SRS to increase their sensing efficiency as a whole. In this dissertation, we present a virtual structure formation control synthesis for a multi-robot system. Control is synthesized through the use of evolutionary robotics, from where the desired collective behavior emerges, while displaying key-features such as fault tolerance and robustness. Initial experiments on formation control synthesis were conducted in simulation environment. We later developed an inexpensive aquatic robotic platform in order to conduct experiments in real world conditions. Our results demonstrated that it is possible to synthesize formation control for a multi-robot system making use of evolutionary robotics. The developed robotic platform was used in several scientific studies.As formações consistem na organização de objetos ou entidades de acordo com um padrão pré-definido. Elas podem ser encontradas na natureza, em animais sociais tais como peixes ou colónias de insetos, onde a organização espontânea em estruturas se verifica. As formações aplicam-se em diversos contextos, tais como cenários militares ou de aplicação da lei, onde são utilizadas para aumentar a performance operacional. O conceito está também presente em desportos coletivos tais como o futebol, onde as formações são utilizadas como estratégia para aumentar a eficiência das equipas. Os enxames de robots são uma abordagem para o estudo de sistemas multi-robô compostos de um grande número de unidades simples, inspirado na organização de sociedades animais. Estes têm um elevado potencial na resolução de tarefas demasiado complexas para um único robot. Quando aplicadas na coordenação deste tipo de sistemas, as formações permitem o movimento coordenado e o aumento da sensibilidade do enxame como um todo. Nesta dissertação apresentamos a síntese de controlo de formação para um sistema multi-robô. O controlo é sintetizado através do uso de robótica evolucionária, de onde o comportamento coletivo emerge, demonstrando ainda funcionalidadeschave tais como tolerância a falhas e robustez. As experiências iniciais na síntese de controlo foram realizadas em simulação. Mais tarde foi desenvolvida uma plataforma robótica para a condução de experiências no mundo real. Os nossos resultados demonstram que é possível sintetizar controlo de formação para um sistema multi-robô, utilizando técnicas de robótica evolucionária. A plataforma desenvolvida foi ainda utilizada em diversos estudos científicos

Towards an autonomous rescue protocol for the Marsu fleet.

Energy autonomy is one of the main challenges in robot exploration. The Marsu fleet, a marsupial robot society, is composed of two kinds of robots, the Marsu-bots (explorers) and the Mother-bot (a mobile charging station). The Marsu-bots are able to connect to each other and recharge batteries if necessary. A protocol will be implemented on the Marsu fleet so when a Marsu-bot runs out of battery another Marsu-bot in the fleet will move to its location and recharge its battery. Such a protocol has two main tasks: first, determine how good a robot may be at solving this task, and second, create a distributed algorithm that allows the Marsu-bots to autonomously select the best one. In this paper it will be shown that leader election algorithms can be used to implement the negotiation. It will also be shown that, while it is imposible to accurately predict the performance of a rescue operation based on distance and battery alone, it is posible to find a cost function that allows the selection of a robot able to perform the rescue operation whithout further assitance. Rules for defining the specific function to be used according to the scenario in which the robots are present will also be given

A Tread/Limb/Serpentine Hybrid Robot: Toward Hypermobility in Deconstructed Environments

According to the Red Cross, an average of over 600 disasters and 100,000 associated deaths occur annually throughout the world. This frequency of disasters strains an already overburdened disaster response effort. In the first 48 hours of a rescue operation, it is estimated that a responder will get less than three hours of continuous sleep as they need to work at full force to set up the operation and begin work in the field. This leads to sleep deprivation during the most critical time for search and rescue of victims. Therefore, robots are greatly needed as a force multiplier in USAR response to reduce some of the burden and workload placed on the human rescue workers to make for a more efficient and effective response