Localization is one of the most important capabilities of mobile robots. Thanks to the fast development of embedded computing hardware in recent years, many localization solutions, such as simultaneous localization and mapping (SLAM), have been vastly investigated. However, popular localization solutions rely heavily on the working environment and are not applicable to scenarios such as search and rescue in the wild, where the working environment is not accessible before the localization operation or where the environment lacks information on features and textures. The thesis thus proposes a design for an innovative localization sensor and a collaborative pose estimation scheme using the localization sensor in order to alleviate the reliance on information from the environment, while providing reliable and accurate pose estimates for mobile robots. The proposed collaborative pose estimation scheme is comprised of individual and collaborative landmark position estimation, localization sensor inter-calibration, and collaborative sensor pose estimation, among which the inter-calibration process ensures that the sensor provides capability to also estimate orientations. In the collaborative scheme, multiple instances of the proposed sensor collaborate to estimate their respective poses by measuring the relative distance and angle among them, where the measurement errors are characterized as Gaussian white noise. Two instances of the proposed localization sensor are implemented, and the collaborative scheme is tested with the instances in the thesis. Both sensor instances reliably and accurately estimate the position of a stationary landmark, and it is demonstrated that the collaboratively estimated position estimate is more accurate than its individual counterpart. Additionally, the two instances also demonstrate their ability to track and estimate the position of a moving landmark. Lastly, the inter-calibration is experimentally validated with the instances with satisfactory performance. The experimental results presented in this work confirm the feasibility and usability of the proposed collaborative pose estimation scheme in a wide range of robotic applications.

Ο σκοπός αυτής της διατριβής είναι να αναπτυχθούν σχέδια συντονισμού σχετικά με την κίνηση των ρομποτικών πρακτόρων με σκοπό την κάλυψη μιας περιοχής κάτω από RF επικοινωνιακούς περιορισμούς . Οι κόμβοι εκτελούν την κίνηση σε ξεχωριστά χρονικά βήματα σύμφωνα με τις διανεμημένες πληροφορίες που αποκτώνται από τους κόμβους που συνδέονται στον προκαθορισμένο αριθμό hops έως ότου φθάσουν στη βέλτιστη τοπολογία όσον αφορά την κάλυψη της περιοχής. Τα ρομπότ υποτίθεται ότι είναι εξοπλισμένα με έναν αισθητήρα για λόγους κάλυψης και με έναν ράδιο πομποδέκτη έτσι ώστε να μεταδοθούν οι πληροφορίες. Ωστόσο, η ακτίνα επικοινωνίας δεν απαιτείται να είναι τουλάχιστον διπλάσια της ακτίνας του αισθητήρα επισκόπησης, κάτι που προσθέτει έναν πρόσθετο περιορισμό στο γενικό πρόβλημα. Τα σχέδια συντονισμού αναπτύσσονται εξασφαλίζοντας την συνολική RF συνδεσιμότητα του δικτύου επιτυγχάνοντας τη βέλτιστη κάλυψη περιοχής. Τα αποτελέσματα ελέγχονται περαιτέρω μέσω των μελετών προσομοιώσεων. / The purpose of this thesis is to develop coordination schemes concerning the motion of robotic agents for area coverage purposes under RF communications constraints. The nodes perform motion in discrete time steps according to distributed information acquired from nodes which are connected at predefined number of hops until they reach optimum area configuration. Robots are supposed to be equipped with sensor for coverage purposes and with radio transceiver so as information to be transmitted. However, communication radius is not demanded to be at least equal to twice the sensing one, imposing an extra constraint in the overall problem. Coordination schemes are developed ensuring end-to-end RF connectivity of the network while attaining optimum area coverage. Results are further verified via simulations studies.

Human-swarm interaction studies how human beings can interact with a robotswarm---a large number of robots cooperating with each other without any form of centralizedcontrol. In today's human-swarm interaction literature, the large majority of the works investigatehow human beings can issue commands to and receive feedback from a robot swarm. However, only a few ofthese works study the effect of the interaction with a robot swarm on human psychology (e.g. on thehuman stress or on the human workload). Understanding human psychology in human-swarm interaction isimportant because the human psychological state can have significant impact on the way humansinteract with robot swarms (e.g. a high level of stress can cause a human operator to freeze in themiddle of a critical task, such as a search-and-rescue task). Most existing works that study human psychology in human-swarm interaction conduct their experimentsusing robot swarms simulated on a computer screen. The use of simulation is convenient becauseexperimental conditions can be repeated perfectly in different experimental runs and becauseexperimentation using real robots is expensive both in money and time. However, simulation suffersfrom the so-called reality gap: the inherent discrepancy between simulation and reality. Itis therefore important to study whether this inherent discrepancy can affect humanpsychology---human operators interacting with a simulated robot swarm can react differently thanwhen interacting with a real robot swarm.A large literature in human-robot interaction has studied the psychological impact of theinteraction between human beings and single robots. This literature could in principle be highlyrelevant to human-swarm interaction. However, an inherent difference between human-robot interactionand human-swarm interaction is that in the latter, human operators interact with a large number ofrobots. This large number of robots can affect human psychology---human operators interacting with alarge number of robots can react differently than when interacting with a single robot or with asmall number of robots. It is therefore important to understand whether the large number of robotsthat composes a robot swarm affects human psychology. In fact, if this is the case, it would not bepossible to directly apply the results of human-robot interaction research to human-swarminteraction.We conducted several experiments in order to understand the effect of the reality gap and the effectof the group size (i.e. the number of robots that composes a robot swarm) on the humanpsychological state. In these experiments our participants are exposed to swarms of robots and arepurely passive---they do not issue commands nor receive feedback from the robots. Making theinteraction passive allowed us to study the effects of the reality gap and of the group size on thehuman psychological state without the risk that an interaction interface (such as a joystick)influences the psychological responses of the participants (and thus limiting the visibility of both thereality gap and group size effects). In the reality gap experiments, participants are exposed tosimulated robot swarms displayed either on a computer screen or in a virtual reality environment, and toreal robot swarms. In the group size experiments, participants are exposed to an increasing numberof real robots.In this thesis, we show that the reality gap and the group size affect the human psychological stateby collecting psychophysiological measures (heart rate and skin conductance), self-reported (viaquestionnaires) affective state measures (arousal and valence), self-reported workload (the amountof mental resource needed to carry out a task) and reaction time (the time needed to respond to astimulus). Firstly, we show with our results that our participants' psychophysiological measures,affective state measures, workload and reaction time are significantly higher when they interactwith a real robot swarm compared to when they interact with a robot swarm simulated on a computerscreen, confirming that the reality gap significantly affects the human psychological state.Moreover, we show that it is possible to mitigate the effect of the reality gap using virtualreality---our participants' arousal, workload and reaction time are significantly higher when theyinteract with a simulated robot swarm displayed in a virtual reality environment as opposed to whenit is displayed on a computer screen. Secondly, we show that our participants' psychophysiologicalmeasures and affective state measures increase when the number of robots they are exposed toincreases. Our results have important implications for research in human-swarm interaction. Firstly, for thefirst time, we show that experiments in simulation change the human psychological state compared toexperiments with real robots. Secondly, we show that a characteristic that is inherent to thedefinition of swarm robotics---the large number of robots that composes a robotswarm---significantly affects the human psychological state. Finally, our results show thatpsychophysiological measures, such as heart rate and skin conductance, provide researchers with moreinformation on human psychology than the information provided by using traditional self-reportedmeasures (collected via psychological questionnaires). / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

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Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the swarm system is designed to work as an independent entity. The swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.

Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the swarm system is designed to work as an independent entity. The swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.

Area exploration and information gathering are one of the fundamental problems in mobile robotics. Much of the current research in swarm robotics is aimed at developing practical solutions to this problem. Exploring large environments poses three main challenges. Firstly, there is the problem of limited connectivity among the robots. Secondly, each of the robots has a limited battery life which requires the robots to be recharged each time they are running out of charge. Lastly, the robots have limited memory to store data. In this work, we mainly focus on the memory and energy constraints of the robot swarm. The memory constraint forces the robots to travel to a centralized data collection center called sink, to deposit data each time their memory is full. The energy constraint forces the robots to travel to the charging station called dock to recharge when their battery level is low. However, this navigation plan is inefficient in terms of energy and time. There is additional energy dissipation in depositing data at the centralized sink. Moreover, ample amount of time is spent in traveling from one end of the arena to the sink owing to the memory constraint. The goal is that the robots perform data gathering in the least time possible with the optimal use of energy. Both the energy and time spent while depositing data at the sink act as an additional overhead cost to this goal. In this work, we propose to study an algorithm to tackle this scenario in a decentralized manner. We implement a dynamic task allocation algorithm which accomplishes the goal of exploration with data gathering by assigning roles to robots based on their memory buffer and energy levels. The algorithm assigns two sets of roles, to the entire group of robots, namely: Role A is the data gatherer, a robot which does the task of workspace exploration and data gathering, and Role B is data relayer, a robot which does the task of data transportation from data gatherers to the sink. By this division of labor, the robots dynamically decide which role to choose given the contradicting goals of maximizing data gathering and minimizing energy loss. The choice of a robot to perform the task of data gathering or data relaying is the key problem tackled in this work. We study the performance of the algorithm in terms of task distribution, time spent by the robots on each task and data throughput. We analyze the behavior of the robot swarm by varying the energy constraints, timeout parameter as well as strategies for relayer choice. We also test whether the algorithm is scalable.

In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of the number of false positives and the time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot. The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically. Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing. Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates. We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults.

We investigate the use of self-assembly in a robotic system as a means of responding to dierent environmental contingencies. Self-assembly is the mechanism through which agents in a multi-robot system autonomously form connections with one another to create larger composite robotic entities. Initially, we consider a simple response mechanism that uses stochastic self-assembly without any explicit control over the resulting morphology | the robots self-assemble into a larger, randomly shaped composite entity if the task they encounter is beyond the physical capabilities of a single robot. We present distributed behavioural control that enables a group of robots to make this collective decision about when and if to self-assemble in the context of a hill crossing task. In a series of real-world experiments, we analyse the eect of dierent distributed timing and decision strategies on system performance. Outside of a task execution context, we present fully decentralised behavioural control capable of creating periodically repeating global morphologies. We then show how arbitrary morphologies can be generated by abstracting our behavioural control into a morphology control language and adding symbolic communication between connected agents. Finally, we integrate our earlier distributed response mechanism into the morphology control language. We run simulated and real-world experiments to demonstrate a self-assembling robotic system that can respond to varying environmental contingencies by forming dierent appropriate morphologies.

In swarm robotics, the control of a group of robots is often fully distributed and does not rely on any leader. In this thesis, we are interested in understanding how to design collective decision making processes in such groups. Our approach consists in taking inspiration from nature, and especially from self organization in social insects, in order to produce effective collective behaviors in robot swarms. We have devised four robotics experiments that allow us to study multiple facets of collective decision making. The problems on which we focus include cooperative transport of objects, robot localization, resource selection, and resource discrimination. We study how information is transferred inside the groups, how collective decisions arise, and through which particular interactions. Important properties of the groups such as scalability, robustness, and adaptivity are also investigated. We show that collective decisions in robot swarms can effectively arise thanks to simple mechanisms of imitation and amplification. We experimentally demonstrate their implementation with direct or indirect information transfer, and with robots that can distinguish the available options partially or not at all.