In this dissertation, we study multi-robot coordination in the context of multi-target tracking. Specifically, we are interested in the coordination achieved by means of submodular function optimization. Submodularity encodes the diminishing returns property that arises in multi-robot coordination. For example, the marginal gain of assigning an additional robot to track the same target diminishes as the number of robots assigned increases. The advantage of formulating coordination problems as submodular optimization is that a simple, greedy algorithm is guaranteed to give a good performance. However, often this comes at the expense of unrealistic models and assumptions. For example, the standard formulation does not take into account the fact that robots may fail, either randomly or due to adversarial attacks. When operating in uncertain conditions, we typically seek to optimize the expected performance. However, this does not give any flexibility for a user to seek conservative or aggressive behaviors from the team of robots. Furthermore, most coordination algorithms force robots to communicate at each time step, even though they may not need to. Our goal in this dissertation is to overcome these limitations by devising coordination algorithms that are parsimonious in communication, allow a user to manage the risk of the robot performance, and are resilient to worst-case robot failures and attacks. In the first part of this dissertation, we focus on designing parsimonious communication strategies for target tracking. Specifically, we investigate the problem of determining when to communicate and who to communicate with. When the robots use range sensors, the tracking performance is a function of the relative positions of the robots and the targets. We propose a self-triggered communication strategy in which a robot communicates its own position with its neighbors only when a certain set of conditions are violated. We prove that this strategy converges to the optimal robot positions for tracking a single target and in practice, reduces the number of communication messages by 30%. When tracking multiple targets, we can reduce the communication by forming subsets of robots and assigning one subset to track a target. We investigate a number of measures for tracking quality based on the observability matrix and show which ones are submodular and which ones are not. For non-submodular measures, we show a greedy algorithm gives a 1/(n+1) approximation, if we restrict the subset to n robots. In optimizing submodular functions, a common assumption is that the function value is deterministic, which may not hold in practice. For example, the sensor performance may depend on environmental conditions which are not known exactly. In the second part of the dissertation, we design an algorithm for stochastic submodular optimization. The standard formulation for stochastic optimization optimizes the expected performance. However, the expectation is a risk-neutral measure. Instead, we optimize the Conditional Value-at-Risk (CVaR), which allows the user the flexibility of choosing a risk level. We present an algorithm, based on the greedy algorithm, and prove that its performance has bounded suboptimality and improves with running time. We also present an online version of the algorithm to adapt to real-time scenarios. In the third part of this dissertation, we focus on scenarios where a set of robots may fail naturally or due to adversarial attacks. Our objective is to track as many targets as possible, a submodular measure, assuming worst-case robot failures. We present both centralized and distributed resilient tracking algorithms to cope with centralized and distributed communication settings. We prove these algorithms give a constant-factor approximation of the optimal in polynomial running time. / Doctor of Philosophy / Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of these systems are fragile and vulnerable to adversarial attacks and uncertain environmental conditions. In most cases, even if a part of the system fails, the entire system performance can be significantly undermined. As robots start to coexist with humans, we need algorithms that can be trusted under real-world, not just ideal conditions. Thus, this dissertation focuses on enabling security, trustworthiness, and long-term autonomy in robotics and autonomous systems. In particular, we devise coordination algorithms that are resilient to attacks, trustworthy in the face of the uncertain conditions, and allow the long-term operation of multi-robot systems. We evaluate our algorithms through extensive simulations and proof-of-concept experiments. Generally speaking, multi-robot systems form the "physical" layer of Cyber-Physical Sytems (CPS), the Internet of Things (IoT), and Smart City. Thus, our research can find applications in the areas of connected and autonomous vehicles, intelligent transportation, communications and sensor networks, and environmental monitoring in smart cities.

We study the problem of planning a tour for an energy-limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged along the way either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines the best locations to place stationary charging stations. While the problems we study are NP-Hard, we present a practical solution using Generalized TSP that finds the optimal solution. If the UGVs are slower, the algorithm also finds the minimum number of UGVs required to support the UAV mission such that the UAV is not required to wait for the UGV. We present a calibration routine to identify parameters that are needed for our algorithm as well as simulation results that show the running time is acceptable for reasonably sized instances in practice. We evaluate the performance of our algorithm through simulations and proof-of-concept experiments with a fully autonomous system of one UAV and UGV. / Master of Science / Commercially available Unmanned Aerial Vehicles (UAVs), especially multi-rotor aircrafts, have a flight time of less than 30 minutes. However many UAV applications, such as surveillance, package delivery, and infrastructure monitoring, require much longer flight times. To address this problem, we present a system in which an Unmanned Ground Vehicle (UGV) can recharge the UAV during deployments. This thesis studies the problem of finding when, where, and how much to recharge the battery. We also allow for the UGV to recharge while moving from one site to another. We present an algorithm that finds the paths for the UAV and UGV to visit a set of points of interest in the least time possible. We also present algorithms for cases when the UGV is slower than the UAV, and more than one UGV may be required. We evaluate our algorithms through simulations and proof-of-concept experiments.

Motion planning for robot operations is concerned with path planning and trajectory generation. In multi-robot systems, i.e. with multiple robots operating simultaneously in a shared workspace, the motion planning also needs to coordinate the robots' motions to avoid collisions between them. The multi-robot coordination decides the cycle-time for the planned paths and trajectories since it determines to which extend the operations can take place simultaneously without colliding. To obtain the quickest cycle-time, there needs to bean optimal balance between, on the one hand short paths and fast trajectories, and on the other hand possibly longer paths and slower trajectories to allow that the operations take place simultaneously in the shared workspace. Due to the inter-dependencies, it becomes necessary to consider the path planning, trajectory generation and multi-robot coordination together as one optimisation problem in order to find this optimal balance.This thesis focusses on optimising the motion planning for multi-robot material handling systems of sheet metal parts. A methodology to model the relevant aspects of this motion planning problem together as one multi-disciplinary optimisation problem for Simulation based Optimisation (SBO) is proposed. The identified relevant aspects include path planning,trajectory generation, multi-robot coordination, collision-avoidance, motion smoothness, end-effectors' holding force, cycle-time, robot wear, energy efficiency, part deformations, induced stresses in the part, and end-effectors' design. The cycle-time is not always the (only) objective since it is sometimes equally/more important to minimise robot wear, energy consumption, and/or part deformations. Different scenarios for these other objectives are therefore also investigated. Specialised single- and multi-objective algorithms are proposed for optimising the motion planning of these multi-robot systems. This thesis also investigates how to optimise the velocity and acceleration profiles of the coordinated trajectories for multi-robot material handling of sheet metal parts. Another modelling methodology is proposed that is based on a novel mathematical model that parametrises the velocity and acceleration profiles of the trajectories, while including the relevant aspects of the motion planning problem excluding the path planning since the paths are now predefined.This enables generating optimised trajectories that have tailored velocity and acceleration profiles for the specific material handling operations in order to minimise the cycle-time,energy consumption, or deformations of the handled parts.The proposed methodologies are evaluated in different scenarios. This is done for real world industrial case studies that consider the multi-robot material handling of a multi-stage tandem sheet metal press line, which is used in the automotive industry to produce the cars' body panels. The optimisation results show that significant improvements can be obtained compared to the current industrial practice.

Multi-robot task assignment (allocation) involves assigning robots to tasks in order to optimize the entire team’s performances. Until now, one of the most useful non-domain-specific ways to coordinate multi-robot systems is through task allocation mechanisms. This dissertation addresses the classic task assignment problems in which robots and tasks are eventually matched by forming a one-to-one mapping, and their overall performances (e.g., cost, utility, and risk) can be linearly summed. At a high level, this research emphasizes two facets of the multi-robot task assignment, including (1) novel extensions from classic assignment algorithms, and (2) completely newly designed task allocation methods with impressive new features. For the former, we first propose a strongly polynomial assignment sensitivity analysis algorithm as well as a means to measure the assignment uncertainties; after that we propose a novel method to address problems of multi-robot routing and formation morphing, the trajectories of which are obtained from projections of augmenting paths that reside in a new three-dimensional interpretation of embedded matching graphs. For the latter, we present two optimal assignment algorithms that are distributable and suitable for multi-robot task allocation problems: the first one is an anytime assignment algorithm that produces non-decreasing assignment solutions along a series of task-swapping operations, each of which updates the assignment configurations and thus can be interrupted at any moment; the second one is a new market-based algorithm with a novel pricing policy: in contrast to the buyers’ “selfish” bidding behaviors in conventional auction/market-based approaches, we employ a virtual merchant to strategically escalate market prices in order to reach a state of equilibrium that satisfies both the merchant and buyers. Both of these newly developed assignment algorithms have a strongly polynomial running time close to the benchmark algorithms but can be easily decentralized in terms of computation and communication.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-09T12:13:05Z No. of bitstreams: 1 rafaeljosefonsecadesa.pdf: 2699281 bytes, checksum: ca2455c138265324b1a8fcbb6075da41 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-10T12:58:01Z (GMT) No. of bitstreams: 1 rafaeljosefonsecadesa.pdf: 2699281 bytes, checksum: ca2455c138265324b1a8fcbb6075da41 (MD5) / Made available in DSpace on 2017-03-10T12:58:01Z (GMT). No. of bitstreams: 1 rafaeljosefonsecadesa.pdf: 2699281 bytes, checksum: ca2455c138265324b1a8fcbb6075da41 (MD5) Previous issue date: 2016-09-09 / Com a evolução da tecnologia, estão aumentando as aplicabilidades dos robôs em nosso meio. Em alguns casos, a utilização de sistemas com múltiplos robôs autônomos trabalhando em cooperação se torna uma ótima alternativa. Há várias pesquisas em andamento na área de robótica com o intuito de aprimorar estas tarefas. Entre estas pesquisas estão os sistemas de patrulhamento. Neste trabalho, o sistema de patrulhamento utilizando múltiplos robôs é implementado considerando a série de chegada de alertas nas estações de monitoramento e o robô pode andar somente em uma única direção. Devido ao número de estações que podem entrar em alerta e ao número de robôs, o controle desse sistema se torna complexo. Como a finalidade de um sistema de patrulhamento é atender possíveis alertas de invasores, é imprescindível que haja uma resposta rápida do controlador responsável para que um robô logo seja encaminhado com o propósito de atender a esse alerta. No caso de sistemas com múltiplos robôs, é necessário que haja uma coordenação do controlador para que os robôs possam atender o máximo de alertas possíveis em um menor instante de tempo. Para resolver esse problema, foi utilizado um controlador composto por uma técnica inteligente de otimização bioinspirada chamada de “Algoritmo Genético” (AG). Este controlador centraliza todas as decisões de controle dos robôs, sendo responsável por orientá-los em relação aos movimentos e captação de informação. As decisões são tomadas com o intuito de maximizar a recompensa do sistema. Esta recompensa é composta pelo ganho de informação do sistema e por uma penalização gerada pela demora em atender aos alertas ativados. Foram feitas simulações com a intenção de verificar a eficácia desse controlador, comparando-o com um controlador utilizando heurísticas pré-definidas. Essas simulações comprovaram a eficiência do controlador via Algoritmo Genético. Devido ao fato do controlador via AG analisar o sistema como um todo enquanto que o controlador heurístico analisa apenas o estágio atual, foi possível observar que a distribuição dos robôs no mapa permitia um atendimento mais ágil às estações com alerta ativados, assim como uma maior aquisição de informações do local. Outro fato importante foi em relação à complexidade do sistema. Foi notado que quanto maior a complexidade do sistema, ou seja, quanto maior o número de robôs e de estações, melhor era a eficiência do controlador via Algoritmo Genético em relação ao controlador heurístico. / New technologies have been considerable advances, and consequently, thus allows the robot appearance as an integral part of our daily lives. In recent years, the design of cooperative multi-robot systems has become a highly active research area within robotics. Cooperative multi-robot systems (MRS) have received significant attention by the robotics community for the past two decades, because their successful deployment have unquestionable social and economical relevance in many application domain. There are several advantages of using multi-robot systems in different application and task. The development and conception of patrolling methods using multi-robot systems is a scientific area which has a growing interest. This work, the patrol system using multiple robots is implemented considering the series of arrival of alerts in the monitoring stations known and the robot was limited to move in one direction. Due to the large number of stations that can assume alert condition and due to the large number of robots, the system control becomes extremely complex. Patrol systems are usually designed for surveillance. An efficient controller permits a patrol in a way that maximizes their chances of detecting an adversary trying to penetrate through the patrol path. The obvious advantage of multi-robot exploration is its concurrency, which can greatly reduce the time needed for the mission. Coordination among multiple robots is necessary to achieve efficiency in robotic explorations. When working in groups, robots need to coordinate their activities. However, a Genetic Algorithm approach was implemented to carryout an optimized control action provided from the controller. In fact the controller determines the robot's behavior. The decision strategies are implemented in order to maximize the system response. The present work deals with a computational study of controller based on Genetic Algorithm and it comparison with another controller based pre-defined heuristics. The simulation results show the efficiency of the proposed controller based on Genetic Algorithm, when compared with the controller based on heuristics. The right decisions from the controller based on Genetic Algorithm allowed a better distribution of the robots on the map leading to fast service stations with active alert, as well as increased acquisition of location information. Another important fact was regarding the complexity of the system. Also, as a result, it was noticed an excellent efficiency of the controller based on Genetic Algorithm when the existence of the large number of robots and stations.

No description available.

Designing an effective human-robot interaction paradigm is particularly important for complex tasks such as multi-robot manipulation that require the human and robot to work together in a tightly coupled fashion. Although increasing the number of robots can expand the area that the robots can cover within a bounded period of time, a poor human-robot interface will ultimately compromise the performance of the team of robots. However, introducing a human operator to the team of robots, does not automatically improve performance due to the difficulty of teleoperating mobile robots with manipulators. The human operator’s concentration is divided not only among multiple robots but also between controlling each robot’s base and arm. This complexity substantially increases the potential neglect time, since the operator’s inability to effectively attend to each robot during a critical phase of the task leads to a significant degradation in task performance. There are several proven paradigms for increasing the efficacy of human-robot interaction: 1) multimodal interfaces in which the user controls the robots using voice and gesture; 2) configurable interfaces which allow the user to create new commands by demonstrating them; 3) adaptive interfaces which reduce the operator’s workload as necessary through increasing robot autonomy. This dissertation presents an evaluation of the relative benefits of different types of user interfaces for multi-robot systems composed of robots with wheeled bases and three degree of freedom arms. It describes a design for constructing low-cost multi-robot manipulation systems from off the shelf parts. User expertise was measured along three axes (navigation, manipulation, and coordination), and participants who performed above threshold on two out of three dimensions on a calibration task were rated as expert. Our experiments reveal that the relative expertise of the user was the key determinant of the best performing interface paradigm for that user, indicating that good user modiii eling is essential for designing a human-robot interaction system that will be used for an extended period of time. The contributions of the dissertation include: 1) a model for detecting operator distraction from robot motion trajectories; 2) adjustable autonomy paradigms for reducing operator workload; 3) a method for creating coordinated multi-robot behaviors from demonstrations with a single robot; 4) a user modeling approach for identifying expert-novice differences from short teleoperation traces.

Thesis (MScEng)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: RoboCup promotes research in robotics and multi-robot systems (MRS). The RoboCup Small Size League (SSL), in particular, offers an entry level opportunity to take part in this field of study. This thesis presents a starting phase for research in robotics and MRS at Stellenbosch University. It includes the full documentation of the mechanical, electronic and software design of an omni-directional soccer robot for RoboCup SSL. The robot is also meant to operate as a hardware and software development platform for research in MRS. The platform was therefore designed with high-level programming language compatibility, a wide range of connectivity, and modularity in mind. The robot uses a single board computer (SBC) running a Linux operating system to accomplish these objectives. Moreover, a driver class library was written in C++ as a software application interface (API) for future development on the robot platform. The robot was also developed with a particular focus on a distributed control architecture. "Player" was implemented as the middleware, which can be used for communication between multiple robots in a distributed environment. Additionally, three tests were performed to demonstrate the functionality of the prototype: a PI speed control test, a direction accuracy test and a static communication test using the middleware. Recommendations for possible future work are also given. / AFRIKAANSE OPSOMMING: RoboCup bevorder navorsing in robotika en multi-robot-stelsels (MRS). Die RoboCup Klein Liga (KL) bied in die besonder die geleentheid om op intreevlak navorsing te doen in hierdie veld. Hierdie tesis verteenwoordig die eerste fase van navorsing in robotika en MRS by Stellenbosch Universiteit. Dit sluit die volledige dokumentasie van die meganiese, elektroniese en sagteware-ontwerp van ’n omnidireksionele sokker-robot vir die KL in. Die robot is ook veronderstel om te dien as ’n hardeware- en sagteware-ontwikkelingsplatform vir navorsing in MRS. Die platform is dus ontwerp met ’n verskeidenheid van uitbreingsmoontlikhede en modulariteit in gedagte asook die moontlikheid om gebruik te maak van ’n hoë-vlak programmeertaal. Om hierdie doelwitte te bereik, maak die robot gebruik van ’n enkel-bord-rekenaar met ’n Linux bedryfstelsel. Verder was ’n sagteware drywer in C++ geskryf om te dien as ’n sagteware-koppelvlak vir toekomstige ontwikkeling op die robot platform. Die robot is ook ontwikkel met die besondere fokus op ’n gedesentraliseerde beheerstels. Player was geïmplementeer as die middelware, wat gebruik kan word vir kommunikasie tussen verskeie robotte in ’n gedesentralliseerde beheerstelsel. Daar is drie toetse uitgevoer om die funksionaliteit van die prototipe te demonstreer, ’n PI spoed beheer toets, ’n rigting akkuraatheidstoets en ’n statiese kommunikasie toets deur van die middelware gebruik te maak. Aanbevelings vir moontlike toekomstige werk word ook verskaf.

Advances in mobile robot technology allow an increasing variety of applications to be imagined, including: search and rescue, exploration of unknown areas and working with hazardous materials. State of the art robots are able to behave autonomously and without direct human control, using on-board devices to perceive, navigate and reason about the world. Unmanned Aerial Vehicles (UAVs) are particularly well suited to performing advanced sensing tasks by moving rapidly through the environment irrespective of the terrain. Deploying groups of mobile robots offers advantages, such as robustness to individual failures and a reduction in task completion time. However, to operate efficiently these teams require specific approaches to enable the individual agents to cooperate. This thesis proposes coordinated approaches to search scenarios for teams of UAVs. The primary application considered is Wilderness Search and Rescue (WiSaR), although the techniques developed are applicable elsewhere. A novel frontier-based search approach is developed for rotor-craft UAVs, taking advantage of available terrain information to minimise altitude changes during flight. This is accompanied by a lightweight coordination mechanism to enable cooperative behaviour with minimal additional overhead. The concept of a team rendezvous is introduced, at which all team members attend to exchange data. This also provides an ideal opportunity to create a comprehensive team solution to relay newly gathered data to a base station. Furthermore, the delay between sensing and the acquired data becoming available to mission commanders is analysed and a technique proposed for adapting the team to meet a latency requirement. These approaches are evaluated and characterised experimentally through simulation. Coordinated frontier search is shown to outperform greedy walk methods, reducing redundant sensing coverage using only a minimal coordination protocol. Combining the search, rendezvous and relay techniques provides a holistic approach to the deployment of UAV teams, meeting mission objectives without extensive pre-configuration.

Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot's energy level, and the time left to task inactivation.