Distributed Embodied Evolutionary Adaptation of Behaviors in Swarms of Robotic Agents

Abstract : 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
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Iñaki Fernández Pérez. Distributed Embodied Evolutionary Adaptation of Behaviors in Swarms of Robotic Agents. Artificial Intelligence [cs.AI]. Université de Lorraine, 2017. English. ⟨NNT : 2017LORR0300⟩. ⟨tel-01695773v2⟩

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