Fast Biconnectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance

Maintaining a robust communication network plays an important role in the success of a multi-robot team jointly performing an optimization task. A key characteristic of a robust multi-robot system is the ability to repair the communication topology itself in the case of robot failure. In this paper, we focus on the Fast Biconnectivity Restoration (FBR) problem, which aims to repair a connected network to make it biconnected as fast as possible, where a biconnected network is a communication topology that cannot be disconnected by removing one node. We develop a Quadratically Constrained Program (QCP) formulation of the FBR problem, which provides a way to optimally solve the problem. We also propose an approximation algorithm for the FBR problem based on graph theory. By conducting empirical studies, we demonstrate that our proposed approximation algorithm performs close to the optimal while significantly outperforming the existing solutions

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Spéciation guidée par l'environnement‎ : interactions sur des périodes évolutionnaires de communautés de plantes artificielles

Depuis des décades, les chercheurs en Vie Artificielle on créé une pléthore de créatures en utilisant de multiples schémas d’encodage, capacités motrices et aptitudes cognitives. Un motif récurrent, cependant, est que la focalisation est centrée sur les individus à évoluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus évolutionnaire vers des régions de l’espace génétique plus robustes and diverses. Nous avons conçu un modèle morphologique complexe, basé sur les graphes orientés de K. Sims, qui repose sur le moteur physique Bullet pour la précision et utilise des contraintes à 6 Degrés de Liberté pour connecter les paires d’organes. Nous avons ainsi évolué un panel de plantes à l’aspect naturel qui devaient survivre malgré des niveaux de ressources variables induits par une source de lumière mobile et des motifs de pluies saisonnières. En plus de cette expérience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins d’herbe statiques. La complexité de ce modèle, cependant, ne permettait pas la mise a l’échelle d’une évolution de populations et a donc été réduit dans l’expérience suivante, notamment en supprimant le moteur physique. Cela nous a amené à l’exploration de la co-évolution de populations composées d’une unique espèce et ayant la capacité de se reproduire de manière autonome grâce à notre Bail-Out Crossover (Croisement avec Désistement). Bien que les populations résultantes n’ont pas démontré un grand intérêt pour cette aptitude, elles ont néanmoins fourni d’importantes informations sur les mécanismes d’auto-reproduction. Ceux-ci ont été mis en action dans un second modèle inspiré des travaux de Bornhofen. Grâce à sa légèreté, cela nous a permis de traiter non seulement de plus grandes populations (de l’ordre de milliers d’individus) mais aussi de plus longues périodes évolutionnaires (100 années, approximativement 5000 générations). Notre première expérience avec ce modèle s’est concentrée sur la possibilité de reproduire des cas d’école de spéciation (allopatrique, parapatrique, péripatrique) sur cette plate-forme. Grâce à APOGet, une nouvelle procédure de regroupement pour l’extraction en parallèle d’espèces à partir d’un arbre généalogique, nous avons pu affirmer que le système était effectivement capable de spéciation spontanée. Cela nous a conduit à une dernière expérience dans laquelle l’environnement était contrôlé par de la Programmation Génétique Cartésienne (CGP), permettant ainsi une évolution automatique d’une population et des contraintes abiotiques auxquelles elle était confrontée. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survécu à de brutales et imprévisibles variations environnementales. En les comparant à un groupe contrôle c pour lequel les contraintes ont été maintenues faibles et constantes, le groupe évolué e a montré des performances mitigées: dans les deux types de tests, une moitié de e surpassait c qui, à son tour, surpassait la moitié restante de e. Nous avons aussi trouvé une très forte corrélation entre les chutes catastrophiques de population et la performance des évolutions correspondantes. Il en résulte que l’évolution de population dans des environnements hostiles et dynamiques n’est pas une panacée bien que ces expériences en démontrent le potentiel et souligne le besoin d’études ultérieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Sims’ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life

Spéciation guidée par l'environnement‎ : interactions sur des périodes évolutionnaires de communautés de plantes artificielles

Depuis des décades, les chercheurs en Vie Artificielle on créé une pléthore de créatures en utilisant de multiples schémas d’encodage, capacités motrices et aptitudes cognitives. Un motif récurrent, cependant, est que la focalisation est centrée sur les individus à évoluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus évolutionnaire vers des régions de l’espace génétique plus robustes and diverses. Nous avons conçu un modèle morphologique complexe, basé sur les graphes orientés de K. Sims, qui repose sur le moteur physique Bullet pour la précision et utilise des contraintes à 6 Degrés de Liberté pour connecter les paires d’organes. Nous avons ainsi évolué un panel de plantes à l’aspect naturel qui devaient survivre malgré des niveaux de ressources variables induits par une source de lumière mobile et des motifs de pluies saisonnières. En plus de cette expérience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins d’herbe statiques. La complexité de ce modèle, cependant, ne permettait pas la mise a l’échelle d’une évolution de populations et a donc été réduit dans l’expérience suivante, notamment en supprimant le moteur physique. Cela nous a amené à l’exploration de la co-évolution de populations composées d’une unique espèce et ayant la capacité de se reproduire de manière autonome grâce à notre Bail-Out Crossover (Croisement avec Désistement). Bien que les populations résultantes n’ont pas démontré un grand intérêt pour cette aptitude, elles ont néanmoins fourni d’importantes informations sur les mécanismes d’auto-reproduction. Ceux-ci ont été mis en action dans un second modèle inspiré des travaux de Bornhofen. Grâce à sa légèreté, cela nous a permis de traiter non seulement de plus grandes populations (de l’ordre de milliers d’individus) mais aussi de plus longues périodes évolutionnaires (100 années, approximativement 5000 générations). Notre première expérience avec ce modèle s’est concentrée sur la possibilité de reproduire des cas d’école de spéciation (allopatrique, parapatrique, péripatrique) sur cette plate-forme. Grâce à APOGet, une nouvelle procédure de regroupement pour l’extraction en parallèle d’espèces à partir d’un arbre généalogique, nous avons pu affirmer que le système était effectivement capable de spéciation spontanée. Cela nous a conduit à une dernière expérience dans laquelle l’environnement était contrôlé par de la Programmation Génétique Cartésienne (CGP), permettant ainsi une évolution automatique d’une population et des contraintes abiotiques auxquelles elle était confrontée. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survécu à de brutales et imprévisibles variations environnementales. En les comparant à un groupe contrôle c pour lequel les contraintes ont été maintenues faibles et constantes, le groupe évolué e a montré des performances mitigées: dans les deux types de tests, une moitié de e surpassait c qui, à son tour, surpassait la moitié restante de e. Nous avons aussi trouvé une très forte corrélation entre les chutes catastrophiques de population et la performance des évolutions correspondantes. Il en résulte que l’évolution de population dans des environnements hostiles et dynamiques n’est pas une panacée bien que ces expériences en démontrent le potentiel et souligne le besoin d’études ultérieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Sims’ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life

A predictive fault-tolerance framework for IoT systems

As Internet of Things (IoT) systems scale, attributes such as availability, reliability, safety, maintainability, security, and performance become increasingly more important. A key challenge to realise IoT is how to provide a dependable infrastructure for the billions of expected IoT devices. A dependable IoT system is one that can defensibly be trusted to deliver its intended service within a given time period. To define a FT-support solution that is applicable to all IoT systems, it is important that error definition is a generic, language-agnostic process, so that FT can be applied as a software pattern. It must also be interoperable, so that FT support can be easily 'plugged into' any existing IoT system, which is facilitated by an adherence to standards and protocols. Lastly, it is important that FT support is, itself, fault tolerant, so that it can be depended on to provide correct support for IoT systems. The work in this thesis considers how real-time and historical data analysis techniques can be combined to monitor an IoT environment and analyse its short- and long-term data to make the system as resilient to failure as possible. Specifically, complex event processing (CEP) is proposed for real-time error detection based on the analysis of stream data in an IoT system, where errors are defined as nondeterministic finite automata (NFA). For long-term error analysis, machine learning (ML) is proposed to predict when an error is likely to occur and mitigate imminent system faults based on previous experience of erroneous system behaviour in the IoT system. The contribution is threefold: (1) a language-agnostic approach to error definition using NFAs, designed to provide 'FT as a service' for easy deployment and integration into existing IoT systems; (2) an implementation of NFAs on a bespoke CEP system, BoboCEP, that provides distributed, resilient event processing at the network edge via active replication; and (3) a ML approach to intelligent FT that can learn from system errors over time to ensure correct long-term FT support. The proposed solution was evaluated using two vertical-farming testbeds and a dataset from a real-world vertical farm. Results showed that the proposed solution could detect and predict the successful detection and recovery of erroneous system behaviours. A performance analysis of BoboCEP was conducted with favourable results

Harnessing Simulated Data with Graphs

Physically accurate simulations allow for unlimited exploration of arbitrarily crafted environments. From a scientific perspective, digital representations of the real world are useful because they make it easy validate ideas. Virtual sandboxes allow observations to be collected at-will, without intricate setting up for measurements or needing to wait on the manufacturing, shipping, and assembly of physical resources. Simulation techniques can also be utilized over and over again to test the problem without expending costly materials or producing any waste. Remarkably, this freedom to both experiment and generate data becomes even more powerful when considering the rising adoption of data-driven techniques across engineering disciplines. These are systems that aggregate over available samples to model behavior, and thus are better informed when exposed to more data. Naturally, the ability to synthesize limitless data promises to make approaches that benefit from datasets all the more robust and desirable. However, the ability to readily and endlessly produce synthetic examples also introduces several new challenges. Data must be collected in an adaptive format that can capture the complete diversity of states achievable in arbitrary simulated configurations while too remaining amenable to downstream applications. The quantity and zoology of observations must also straddle a range which prevents overfitting but is descriptive enough to produce a robust approach. Pipelines that naively measure virtual scenarios can easily be overwhelmed by trying to sample an infinite set of available configurations. Variations observed across multiple dimensions can quickly lead to a daunting expansion of states, all of which must be processed and solved. These and several other concerns must first be addressed in order to safely leverage the potential of boundless simulated data. In response to these challenges, this thesis proposes to wield graphs in order to instill structure over digitally captured data, and curb the growth of variables. The paradigm of pairing data with graphs introduced in this dissertation serves to enforce consistency, localize operators, and crucially factor out any combinatorial explosion of states. Results demonstrate the effectiveness of this methodology in three distinct areas, each individually offering unique challenges and practical constraints, and together showcasing the generality of the approach. Namely, studies observing state-of-the-art contributions in design for additive manufacturing, side-channel security threats, and large-scale physics based contact simulations are collectively achieved by harnessing simulated datasets with graph algorithms