As the capabilities of autonomous systems have increased so has interest in utilizing teams of autonomous systems to accomplish tasks more efficiently. This dissertation takes steps toward enabling the cooperation of unmanned systems in scenarios that are challenging, such as GPS-denied or perceptually aliased environments. This work begins by developing a cooperative navigation framework that is scalable in the number of agents, robust against communication latency or dropout, and requires little a priori information. Additionally, this framework is designed to be easily adopted by existing single-agent systems with minimal changes to existing software and software architectures. All systems in the framework are validated through Monte Carlo simulations. The second part of this dissertation focuses on making cooperative navigation robust in challenging environments. This work first focuses on enabling a more robust version of pose graph SLAM, called cycle-based pose graph optimization, to be run in real-time by implementing and validating an algorithm to incrementally approximate a minimum cycle basis. A new algorithm is proposed that is tailored to multi-agent systems by approximating the cycle basis of two graphs that have been joined. These algorithms are validated through extensive simulation and hardware experiments. The last part of this dissertation focuses on scenarios where perceptual aliasing and incorrect or unknown data association are present. This work presents a unification of the framework of consistency maximization, and extends the concept of pairwise consistency to group consistency. This work shows that by using group consistency, low-degree-of-freedom measurements can be rejected in high-outlier regimes if the measurements do not fit the distribution of other measurements. The efficacy of this method is verified extensively using both simulation and hardware experiments.

Cette thèse présente une méthode de navigation autonome d’un véhicule routier robotisé dans un contexte de l’interaction conducteur - véhicule, dans lequel le conducteur humain et le système de navigation autonome coopèrent dans le but d’associer les avantages du contrôle manuel et automatique. La navigation du véhicule est réalisée en parallèle par le conducteur humain et le système de conduite automatique, basée sur la perception de l’environnement. La navigation coopérative est basée sur l’analyse et correction des gestes du conducteur humain par le système intelligent, dans le but d’exécuter une tâche de navigation locale qui concerne le suivie de voie avec évitement d’obstacles. L’algorithme d’interaction humain-véhicule est basé sur des composants de navigation référencée capteurs formés par des contrôleurs d’asservissement visuel (VS) et la méthode d’évitement d’obstacle « Dynamic Window Approach (DWA) » basée sur la grilles d’occupation. Ces méthodes prennent en entrée la perception de l’environnement fournie par des capteurs embarqués comprenant un système monovision et un LIDAR. Dû à des impossibilités techniques/légales, nous n’avons pas pu valider nos méthodes sur notre véhicule robotisé (une Renault Zoé robotisée), ainsi nous avons construit des structures « driver-in-theloop » dans des environnements de simulation Matlab et SCANeRTM Studio. En Matlab, le conducteur humain est modélisé par un algorithme appelé « Human Driver Behaviour controller (HDB) », lequel génère des gestes de conduite dangereux dans la partie manuelle de l’entrée de commande du système coopératif. En SCANeR Studio, la sortie de l’HDB est remplacée par des commandes manuelles générées directement par un conducteur humain dans l’interface utilisateur du simulateur. Des résultats de validation dans les deux environnements de simulation montrent la faisabilité et la performance du système de navigation coopérative par rapport aux tâches de suivie de voie, l’évitement d’obstacles et le maintien d’une distance de sécurité. / This thesis presents an approach of cooperative navigation control pattern for intelligent vehicles in the context of human-vehicle interaction, in which human driver and autonomous servoing system cooperate for the purpose of benefiting from mutual advantages of manual and auto control. The navigation of the vehicle is performed in parallel by the driver and the embedded intelligent system, based on the perception of the environment. The cooperative framework we specify concerns the analysis and correction of the human navigation gestures by the intelligent system for the purpose of performing local navigation tasks of road lane following with obstacle avoidance. The human-vehicle interaction algorithm is based on autonomous servoing components as Visual Servoing (VS) controllers and obstacle avoidance method Dynamic Window Approach (DWA) based on Occupancy Grid, which are supported by the environment perception performed carried out by on-boarded sensors including a monovision camera and a LIDAR sensor. Given the technical/legal impossibility of validating our interaction method on our robotic vehicle (a robotic Renault Zoé), the driver-in-the-loop structures of system are designed for simulative environment of both Matlab and SCANeRTM Studio. In Matlab environment human driver is modeled by a code-based Human Driver Behaviour (HDB) Controller, which generates potential dangerous behaviors on purpose as manual control of the cooperative system. In SCANeR Studio environment the HDB is replaced by real-time manual command (a real human driver) via driving interface of this simulator. Results of simulative validation show the feasibility and performance of the cooperative navigation system with respect to tasks of driving security including road lane following, obstacle avoidance and safe distance maintenance.