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.

Every year, certain animal and insect species flock together to make long-range migrations to reach their feeding or breeding grounds. A number of interesting observations can be made regarding this phenomenon. First, individuals tend to create large flocks, which can include millions of individuals in fishes, for these migrations. Second, migrations typically cover long distances. Third, despite all kinds of disturbances affecting the individuals during these migrations, the flocks can reach the very same breeding or feeding grounds with remarkable accuracy. Biological studies indicated that these animals mainly use the magnetic field of earth (among many other environmental cues) to determine the direction of their travel. It was also claimed that migrating in flocks has been the key factor behind the accuracy of reaching the same grounds at the end of the migration. In this thesis, we take a constructivist approach towards investigating the effects of flocking in long-range travels using a swarm of physical and simulated mobile robots. Specifically, we extend a self-organized flocking behavior that was developed by Turgut et al. (2008) that allows the long-range migration of a robotic swarm in space using the magnetic field of the earth. Using this behavior, we analyze how the accuracy of the robotic swarm reaching a particular &quot / breeding ground&quot / is affected by four factors / namely, (1) averaging through the heading alignment, (2) noise in sensing the homing direction, (3) differences in the characteristics of the individuals, and (4) disturbances caused by the proximal interactions of the robots during flocking. Through systematic experiments with physical and simulated robots, we analyze how these factors affect the accuracy along with the flock size and different sources of noise.

"I daresay you haven’t had much practice," said the Queen. "When I was your age, I always did it for half-an-hour a day. Why, sometimes I’ve believed as many as six impossible things before breakfast." Lewis Carroll, Through the Looking-Glass, and What Alice Found There[21]. This thesis examines an artificial proto-culture of e-Puck robots to examine which factors affect the transmission of memes, in the form of sounds imitated back and forth between the robots, to determine which factors promote or inhibit meme diversity and spread. Meme theory posits that the development of cultural artifacts such as ideas, myths, religions, etc. arises naturally from cultural information transfer by imitation. It has been suggested that 'copybots’, robots programmed to imitate each other, would eventually lead to the emergence of something recognizable as culture[13]. This thesis describes part of a research project which sought to use e-Puck robots to implement a copybot based system to examine this proto-culture emergence. The group implemented an Artificial Culture lab for experiments using the e-Puck robots. Here the focus is on the imitation of sound patterns (the memes) within a group of e-Pucks to examine which factors promote or inhibit meme diversity and spread. Other parts of the research group examined the imitation of movement patterns, human perceptions (and preconceptions of robots), and abstract societal level modeling. Within is described a simulator and a series of experiments on the imitation of sounds using that simulator that examine the factors affecting meme transmission in homogeneous populations and evolving heterogeneous populations. These experiments show that they key factor in promoting meme diversity and spread is simply the frequency with which imitation occurs. They also show that memory size plays a smaller role and selection strategy (for choosing which meme to imitate) plays a lesser role still. "If you’ve done six impossible things this morning, why not round it off with breakfast at Milliways, the Restaurant at the End of the Universe." Douglas Adams, The Restaurant at the End of the Universe[1].

A striking feature of swarm robotics is its ability to solve complex tasks through simple local interactions between robots. Those interactions require a good infrastructure in communication and localization. However, in underwater environments, the severe attenuation of radio waves complicates communication and localization of different vehicles. Existing literature on underwater swarms use centralized network topology which require physical vicinity to the central node to ensure reliability. We are interested in building a decentralized underwater swarm with a decentralized network topology that only requires neighbour communication and self-localization. We develop a simple leader-follower interaction rule where the follower estimates the leader's position and acts upon that estimation. The leader shines a 450 nm diffracted blue laser that the follower uses to continuously align its light sensors to the light source. Furthermore, the leader's laser can be modulated for explicit communication purposes. The proposed leader-follower approach produces satisfactory results in surge and sway axes, however, it is not robust against illumination changes in the environment. We then proceed to solve the self-localization problem, by fusing Inertial Measurement Unit (IMU) values with the thrust to estimate a robot’s position. In an Ardusub Simulation in the loop (SITL), the particle filter showed a slightly better performance than the Extended Kalman Filter (EKF) in the surge axis. However, both filters are prone to drifting after a while. We have observed that IMU values need to be filtered properly or another reliable sensor must be used alternatively.

abstract: In certain ant species, groups of ants work together to transport food and materials back to their nests. In some cases, the group exhibits a leader-follower behavior in which a single ant guides the entire group based on its knowledge of the destination. In some cases, the leader role is occupied temporarily by an ant, only to be replaced when an ant with new information arrives. This kind of behavior can be very useful in uncertain environments where robot teams work together to transport a heavy or bulky payload. The purpose of this research was to study ways to implement this behavior on robot teams. In this work, I combined existing dynamical models of collective transport in ants to create a stochastic model that describes these behaviors and can be used to control multi-robot systems to perform collective transport. In this model, each agent transitions stochastically between roles based on the force that it senses the other agents are applying to the load. The agent’s motion is governed by a proportional controller that updates its applied force based on the load velocity. I developed agent-based simulations of this model in NetLogo and explored leader-follower scenarios in which agents receive information about the transport destination by a newly informed agent (leader) joining the team. From these simulations, I derived the mean allocations of agents between “puller” and “lifter” roles and the mean forces applied by the agents throughout the motion. From the simulation results obtained, we show that the mean ratio of lifter to puller populations is approximately 1:1. We also show that agents using the role update procedure based on forces are required to exert less force than agents that select their role based on their position on the load, although both strategies achieve similar transport speeds. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

abstract: Numerous works have addressed the control of multi-robot systems for coverage, mapping, navigation, and task allocation problems. In addition to classical microscopic approaches to multi-robot problems, which model the actions and decisions of individual robots, lately, there has been a focus on macroscopic or Eulerian approaches. In these approaches, the population of robots is represented as a continuum that evolves according to a mean-field model, which is directly designed such that the corresponding robot control policies produce target collective behaviours. This dissertation presents a control-theoretic analysis of three types of mean-field models proposed in the literature for modelling and control of large-scale multi-agent systems, including robotic swarms. These mean-field models are Kolmogorov forward equations of stochastic processes, and their analysis is motivated by the fact that as the number of agents tends to infinity, the empirical measure associated with the agents converges to the solution of these models. Hence, the problem of transporting a swarm of agents from one distribution to another can be posed as a control problem for the forward equation of the process that determines the time evolution of the swarm density. First, this thesis considers the case in which the agents' states evolve on a finite state space according to a continuous-time Markov chain (CTMC), and the forward equation is an ordinary differential equation (ODE). Defining the agents' task transition rates as the control parameters, the finite-time controllability, asymptotic controllability, and stabilization of the forward equation are investigated. Second, the controllability and stabilization problem for systems of advection-diffusion-reaction partial differential equations (PDEs) is studied in the case where the control parameters include the agents' velocity as well as transition rates. Third, this thesis considers a controllability and optimal control problem for the forward equation in the more general case where the agent dynamics are given by a nonlinear discrete-time control system. Beyond these theoretical results, this thesis also considers numerical optimal transport for control-affine systems. It is shown that finite-volume approximations of the associated PDEs lead to well-posed transport problems on graphs as long as the control system is controllable everywhere. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2019

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[pt] Robótica coletiva de enxame é uma abordagem para o controle de sistemas robóticos multi-agentes baseada em insetos sociais e outros sistemas naturais que apresentam características de auto-organização e emergência, com aplicações disruptivas em robótica e inúmeras possibilidades de expansão em outras áreas. Porém, sendo um campo relativamente novo existem poucas plataformas experimentais para seu estudo, e as existentes são, em sua maioria, especialmente desenvolvidas para tarefas e algoritmos específicos. Uma plataforma de estudos genérica para o estudo de sistemas robóticos coletivos é, por si só, uma tarefa tecnológica não trivial além de ser um recurso valioso para um centro de pesquisas interessado em realizar experimentos no assunto. Neste trabalho dois importantes algoritmos de controle colaborativo multi-robôs foram estudados: busca do melhor caminho e transporte coletivo. Uma análise completa dos mecanismos biológicos, dos modelos lógicos e do desenvolvimento dos algoritmos é apresentada. Para realizar os experimentos uma plataforma genérica foi desenvolvida baseada nos robôs móveis “iRobot Create”. Sensores virtuais são implementados em através de um sistema de visão computacional combinado com um simulador em tempo real. O sistema de sensores virtuais permite a incorporação de sensores ideais no sistema experimental, incluindo modelos mais complexos de sensores reais, incluindo a possibilidade da adição de ruído simulador nas leituras. Esta abordagem permite também a utilização de sensores para detecção de objetos virtuais, criados pelo simulador, como paredes virtuais e feromônios virtuais. Cada robô possui um sistema eletrônico embarcado especialmente desenvolvido baseado em micro controlador ARM. A eletrônica adicionada é responsável por receber as leituras dos sensores virtuais através de um link de radio em um protocolo customizado e calcular, localmente, o comportamento do robô. Os algoritmos são implementados na linguagem de alto nível Lua. Mesmo com as leituras dos sensores virtuais sendo transmitidas de um sistema centralizado é importante ressaltar que todo o algoritmo de inteligência é executado localmente por cada agente. As versões modificadas e adaptadas dos algoritmos estudados na plataforma com sensores virtuais foram analisadas, juntamente com suas limitações, e se mostraram compatíveis com os resultados esperados e acessíveis na literatura que utiliza sistemas experimentais mais específicos e mais dispendiosos. Portanto a plataforma desenvolvida se mostra capaz como ferramenta para experimentos em controle de sistemas robóticos multi-agentes com baixo custo de implementação, além da inclusão, através do mecanismo de sensores virtuais, de sensores ainda em desenvolvimento ou comercialmente indisponíveis. / [en] Swarm robotics is an approach to multi-robot control based on social insects and other natural systems, which shows self-organization and emergent characteristics, with disruptive applications on robotics and possibilities in a variety of areas. But, being a relatively new field of research, there are few experimental platforms to its study, and most of them are crafted for very specific tasks and algorithms. A general study platform of swarm robotics, by itself, is a non-trivial technological deed and also a very valuable asset to a research center willing to run experiments on the topic. In this work, two important algorithms in multi-robot collaborative control strategies are studied: path finding and collective transport. A complete analysis of the biological mechanisms, models and computer abstractions that resulted in the development of those algorithms is shown. To perform the multi-robot experiments, several “iRobot Create” mobile robots are employed. Virtual sensors and virtual walls are implemented in real time in the experimental system through cameras and especially developed computer vision software. Virtual sensors allow the incorporation of ideal sensors in the experimental system, including complete models of real sensors, with the possibility of adding virtual noise to the measurements. This approach also allows the use of sensors to detect virtually created objects, such as virtual walls or virtual pheromones. Each physical robot has a customized embedded system, based on the ARM microprocessor, which receives the virtual sensors readings through a radio link in an also customized protocol. The behavior of each autonomous agent is locally calculated using the high-level programming language Lua. Even though the virtual sensor readings are transmitted from an external centralized computer system, all behaviors are locally and independently calculated by each agent. The adaptations of the studied algorithms to the platform with virtual sensors are analyzed, along with its limitations. It is shown that the experimental results using virtual sensors are coherent with results from the literature using very specialized and expensive robot/sensor setups. Therefore, the developed platform is able to experimentally study new control strategies and swarm algorithms with a low setup cost, including the possibility of virtually incorporating sensors that are still under development or not yet commercially available.