The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that can not be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. In order to keep individual robots simple, a control algorithm has to keep expensive communication to a minimum and has to be able to act on little information to keep the amount of sensors down. The number of sensors and actuators can be reduced even more when necessary capabilities are spread out over different agents that then combine them by cooperating. Self-organised differentiation within these heterogeneous groups has to take the individual abilities of agents into account to improve group performance. In this thesis it is shown that a homogeneous group of versatile agents can not be easily replaced by a heterogeneous group, by separating the abilities of the versatile agents into several specialists. It is shown that no composition of those specialists produces the same outcome as a homogeneous group on a clustering task. In the second part of this work, an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is analysed in more detail. It does not require communication and needs only the information on individual success or failure. The algorithm leads to self-organised regulation of group activity depending on object availability in the environment by adjusting resting times in a base. A possible variation of this algorithm is introduced which replaces the probabilistic mechanism with which agents determine to leave the base. It is demonstrated that a direct calculation of the resting times does not lead to differences in terms of differentiation and speed of adaptation. After investigating effects of different parameters on the system, it is shown that there is no efficiency increase in static environments with constant object density when using a homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic environments. The algorithm was also reported to lead to higher activity of agents which have higher performance. It is shown that this leads to efficiency gains in heterogeneous groups in static and dynamic environments.

Traditionally robots are controlled using devices like joysticks, keyboards, mice and other similar human computer interface (HCI) devices. Although this approach is effective and practical for some cases, it is restrictive only to healthy individuals without disabilities, and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI). This work presents a novel concept of using human bio-signals to control swarms of robots. With this concept there are two major advantages: Firstly, it gives amputees and people with certain disabilities the ability to control robotic swarms, which has previously not been possible. Secondly, it also gives the user a more intuitive interface to control swarms of robots by using gestures, thoughts, and eye movement. We measure different bio-signals from the human body including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf products. After minimal signal processing, we then decode the intended control action using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest Neighbors (K-NN). We employ formation controllers based on distance and displacement to control the shape and motion of the robotic swarm. Comparison for ground truth for thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles.

Les essaims de robots sont des systèmes composés d’un grand nombre de robots relativement simples. Du fait du grand nombre d’unités, ces systèmes ont de bonnes propriétés de robustesse et de passage à l’échelle. Néanmoins, il reste en général difficile de concevoir manuellement des contrôleurs pour les essaims de robots, à cause de la grande complexité des interactions inter-robot. Par conséquent, les approches automatisées pour l’apprentissage de comportements d’essaims de robots constituent une alternative attrayante. Dans cette thèse, nous étudions l’adaptation de comportements d’essaim de robots avec des méthodes de Embodied Evolutionary Robotics (EER) distribuée. Ainsi, nous fournissons trois contributions principales : (1) Nous étudions l’influence de la pression à la sélection dirigée vers une tâche dans un essaim d’agents robotiques qui utilisent une approche d’EER distribuée. Nous évaluons l’impact de différents opérateurs de sélection dans un algorithme d’EER distribuée pour un essaim de robots. Nos résultats montrent que le plus forte la pression à la sélection est, les meilleures performances sont atteintes lorsque les robots doivent s’adapter à des tâches particulières. (2) Nous étudions l’évolution de comportements collaboratifs pour une tâche de récolte d’objets dans un essaim d’agents robotiques qui utilisent une approche d’EER distribuée. Nous réalisons un ensemble d’expériences où un essaim de robots s’adapte à une tâche collaborative avec un algorithme d’EER distribuée. Nos résultats montrent que l’essaim s’adapte à résoudre la tâche, et nous identifions des limitations concernant le choix d’action. (3) Nous proposons et validons expérimentalement un mécanisme complètement distribué pour adapter la structure des neurocontrôleurs des robots dans un essaim qui utilise une approche d’EER distribuée, ce qui permettrait aux neurocontrôleurs d’augmenter leur expressivité. Nos expériences montrent que notre mécanisme, qui est complètement décentralisé, fournit des résultats similaires à un mécanisme qui dépend d’une information globale / Robot swarms are systems composed of a large number of rather simple robots. Due to the large number of units, these systems, have good properties concerning robustness and scalability, among others. However, it remains generally difficult to design controllers for such robotic systems, particularly due to the complexity of inter-robot interactions. Consequently, automatic approaches to synthesize behavior in robot swarms are a compelling alternative. In this thesis, we focus on online behavior adaptation in a swarm of robots using distributed Embodied Evolutionary Robotics (EER) methods. To this end, we provide three main contributions: (1) We investigate the influence of task-driven selection pressure in a swarm of robotic agents using a distributed EER approach. We evaluate the impact of a range of selection pressure strength on the performance of a distributed EER algorithm. The results show that the stronger the task-driven selection pressure, the better the performances obtained when addressing given tasks. (2) We investigate the evolution of collaborative behaviors in a swarm of robotic agents using a distributed EER approach. We perform a set of experiments for a swarm of robots to adapt to a collaborative item collection task that cannot be solved by a single robot. Our results show that the swarm learns to collaborate to solve the task using a distributed approach, and we identify some inefficiencies regarding learning to choose actions. (3) We propose and experimentally validate a completely distributed mechanism that allows to learn the structure and parameters of the robot neurocontrollers in a swarm using a distributed EER approach, which allows for the robot controllers to augment their expressivity. Our experiments show that our fully-decentralized mechanism leads to similar results as a mechanism that depends on global information

This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA. Firstly, mEDEA is extended with an explicit relative fitness measure while still maintaining the distributed nature of the algorithm. Two ways of using the relative fitness are investigated: influencing the spreading of genomes and performing an explicit genome selection. Both methods lead to an improvement in the swarm's abilityto maintain energy over longer periods. Secondly, a communication energy model is derived and introduced into the simulator to investigate the influence of accounting for the costs of communication in the distributed evolutionary algorithm where communication is a key component. Thirdly, a method is introduced that relates environmental conditions to a measure of the swarm's behaviour in a 3-dimensional map to study the environment's influence on the emergence of behaviours at the individual and swarm level. Interesting regions for further experimentation are identified in which algorithm specific characteristics show effect and can be explored. Finally, a novel individual learning method is developed and used to investigate how the most effective balance between evolutionary and lifetime-adaptation mechanisms is influenced by aspects of the environment a swarm operates in. The results show a clearlink between the effectiveness of different adaptation mechanisms and environmental conditions, specifically the rate of change and the availability of learning opportunities.

Self-organized aggregation is the global level gathering of randomly placed robots using local sensing. Developing high performance and scalable aggregation behaviors for a swarm of mobile robots is non-trivial and still in need, when robots control themselves, perceive only a small part of the arena, and do not have access to information such as their position, the size of the arena or the number of robots. In this thesis, we developed a non-spatial probabilistic geometric model for self-organized aggregation as a tool to analyze aggregation. The model consists of four formulas for predicting the probabilities of aggregation events: creation, growing, shrinking and dissipation of an aggregate. The creation probability is derived mathematically using kinetic theory of gases. In order to derive formulas for growing, shrinking and dissipation probabilities, first, it is assumed that aggregates formed by robots are circular. Then, these formulas are derived geometrically using circle packing theory. We proposed an aggregation behavior and implemented this behavior in the Stage multi-robot simulator. The behavior consists of four sub-behaviors: search, wait, leave and change direction. The wait sub-behavior is specially designed to force aggregates to be circular so that our assumption for the model holds in simulation experiments. We verified each formula using simulation experiments conducted in the Stage multi-robot simulator. Through systematic experiments, we showed that model predictions and simulation results match well and the formulas proposed for growing and shrinking probabilities predict these probabilities better for larger aggregates compared to predictions of previous self-organized aggregation models. We also conducted experiments, in which certain aggregation events are disabled systematically, in order to verify the model further and show that our model can be used to predict the steady-state performance of generic simulation experiments. We use two different methods to predict the steady state performance with our model: microscopic model execution and steady state analysis. It is shown that the largest aggregate size, the number of aggregates, the number of searching robots and the aggregate distributions at the steady state-obtained from microscopic model execution, steady state analysis and simulation experiments are close to each other and our model can be used to predict steady-state performance of aggregation experiments.

In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performance, as measured by these two metrics, change 1) with transition probabilities, 2) with number of simulation steps, and 3) with arena size, is studied.

abstract: This thesis considers two problems in the control of robotic swarms. Firstly, it addresses a trajectory planning and task allocation problem for a swarm of resource-constrained robots that cannot localize or communicate with each other and that exhibit stochasticity in their motion and task switching policies. We model the population dynamics of the robotic swarm as a set of advection-diffusion- reaction (ADR) partial differential equations (PDEs). Specifically, we consider a linear parabolic PDE model that is bilinear in the robots' velocity and task-switching rates. These parameters constitute a set of time-dependent control variables that can be optimized and transmitted to the robots prior to their deployment or broadcasted in real time. The planning and allocation problem can then be formulated as a PDE-constrained optimization problem, which we solve using techniques from optimal control. Simulations of a commercial pollination scenario validate the ability of our control approach to drive a robotic swarm to achieve predefined spatial distributions of activity over a closed domain, which may contain obstacles. Secondly, we consider a mapping problem wherein a robotic swarm is deployed over a closed domain and it is necessary to reconstruct the unknown spatial distribution of a feature of interest. The ADR-based primitives result in a coefficient identification problem for the corresponding system of PDEs. To deal with the inherent ill-posedness of the problem, we frame it as an optimization problem. We validate our approach through simulations and show that reconstruction of the spatially-dependent coefficient can be achieved with considerable accuracy using temporal information alone. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2014

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2017

The study of large-scale decentralised systems composed of numerous interacting agents that self-organise to perform a common task is receiving growing attention in several application domains. However, real world implementations are limited by a lack of well-established design methodologies that provide performance guarantees. Engineering such systems is a challenging task because of the difficulties to obtain the micro-macro link: a correspondence between the microscopic description of the individual agent behaviour and the macroscopic models that describe the system's dynamics at the global level. In this thesis, we propose an engineering methodology for designing decentralised systems, based on the concept of design patterns. A design pattern provides a general solution to a specific class of problems which are relevant in several application domains. The main component of the solution consists of a multi-level description of the collective process, from macro to micro models, accompanied by rules for converting the model parameters between description levels. In other words, the design pattern provides a formal description of the micro-macro link for a process that tackles a specific class of problems. Additionally, a design pattern provides a set of case studies to illustrate possible implementation alternatives both for simple or particularly challenging scenarios. We present a design pattern for the best-of-n, decentralised decision problem that is derived from a model of nest-site selection in honeybees. We present two case studies to showcase the design pattern usage in (i) a multiagent system interacting through a fully-connected network, and (ii) a swarm of particles moving on a bidimensional plane. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

The main issue in swarm robotics is to design the behavior of the individual robots so that a desired collective behavior is achieved. A promising alternative to the classical trial-and-error design approach is to rely on automatic design methods. In an automatic design method, the problem of designing the control software for a robot swarm is cast into an optimization problem: the different design choices define a search space that is explored using an optimization algorithm. Most of the automatic design methods proposed so far belong to the framework of evolutionary robotics. Traditionally, in evolutionary robotics the control software is based on artificial neural networks and is optimized automatically via an evolutionary algorithm, following a process inspired by natural evolution. Evolutionary robotics has been successfully adopted to design robot swarms that perform various tasks. The results achieved show that automatic design is a viable and promising approach to designing the control software of robot swarms. Despite these successes, a widely recognized problem of evolutionary robotics is the difficulty to overcome the reality gap, that is, having a seamless transition from simulation to the real world. In this thesis, we aim at conceiving an effective automatic design approach that is able to deliver robot swarms that have high performance once deployed in the real world. To this, we consider the major problem in the automatic design of robot swarms: the reality gap problem. We analyze the reality gap problem from a machine learning perspective. We show that the reality gap problem bears a strong resemblance to the generalization problem encountered in supervised learning. By casting the reality gap problem into the bias-variance tradeoff, we show that the inability to overcome the reality gap experienced in evolutionary robotics could be explained by the excessive representational power of the control architecture adopted. Consequently, we propose AutoMoDe, a novel automatic design approach that adopts a control architecture with low representational power. AutoMoDe designs software in the form of a probabilistic finite state machine that is composed automatically starting from a number of pre-existing parametric modules. In the experimental analysis presented in this thesis, we show that adopting a control architecture that features a low representational power is beneficial: AutoMoDe performs better than an evolutionary approach. Moreover, AutoMoDe is able to design robot swarms that perform better that the ones designed by human designers. AutoMoDe is the first automatic design approach that it is shown to outperform human designers in a controlled experiment. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished