Evolving Test Environments to Identify Faults in Swarm Robotics Algorithms

Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications

Multi-Robot Adversarial Resilience using Control Barrier Functions

In this paper we present a control barrier functionbased (CBF) resilience controller that provides resilience in a multi-robot network to adversaries. Previous approaches provide resilience by virtue of specific linear combinations of multiple control constraints. These combinations can be difficult to find and are sensitive to the addition of new constraints. Unlike previous approaches, the proposed CBF provides network resilience and is easily amenable to multiple other control constraints, such as collision and obstacle avoidance. The inclusion of such constraints is essential in order to implement a resilience controller on realistic robot platforms. We demonstrate the viability of the CBF-based resilience controller on real robotic systems through case studies on a multi-robot flocking problem in cluttered environments with the presence of adversarial robots

Um método robusto aplicado no controle de formação e rastreamento de trajetória de um conjunto de robôs móveis não-holonômicos com dinâmica incerta/ A robust method applied to the formation and trajectory tracking control of a set of nonholonomic mobile robots with uncertain dynamics

Este artigo propõe a implementação do método de Controle com Rejeição Ativa de Distúrbios (ADRC) com planta modificada em uma estratégia de controle em cascata. O objetivo é realizar a formação e o controle de rastreamento de uma equipe de robôs móveis não-holonômicos com parâmetros dinâmicos incertos. Ao contrário do esquema ADRC padrão que requer um ganho de controle conhecido, o controlador ADRC modificado proposto neste artigo usa uma nova descrição entrada/saída da planta para a estrutura de cada modelo dinâmico de robô. Assim, ao introduzir essa modificação, é possível projetar um controlador robusto sem exigir o conhecimento exato sobre o ganho de controle do sistema. Resultados de simulações computacionais são apresentados para mostrar a eficiência da estratégia proposta

Distributed vs. Centralized Particle Swarm Optimization for Learning Flocking Behaviors

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neural network. Two learning strategies are applied: homogeneous centralized learning, in which every robot runs the same controller and the performance is evaluated externally with a global metric, and heterogeneous distributed learning, in which robots run different controllers and the performance is evaluated independently on each robot with a local metric. The two sets of metrics enforce Reynolds’ flocking rules, resulting in a good correspondence between the metrics and the flocking behaviors obtained. Results demonstrate that it is possible to learn the collective task using both learning approaches. The solutions from the centralized learning have higher fitness and lower standard deviation than those learned in a distributed manner. We test the learned controllers in real robot experiments and also show in simulation the performance of the controllers with increasing number of robots

Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments

Particle swarm control

International audienceControlling several and possibly independent moving agents in order to reach global goals is a tedious task that has applications in many engineering fields such as robotics or computer animation. Together, the different agents form a whole called swarm, which may display interesting collective behaviors. When the agents are driven by their own dynamics, controlling this swarm is known as the particle swarm control problem. In that context, several strategies, based on the control of individuals using simple rules, exist. This paper defends a new and original method based on a centralized approach. More precisely, we propose a framework to control several particles with constraints either expressed on a per-particle basis, or expressed as a function of their environment. We refer to these two categories as respectively Lagrangian or Eulerian constraints. The contributions of the paper are the following: (i) we show how to use optimal control recipes to express an optimization process over a large state space including the dynamic information of the particles; and (ii) the relation between the Lagrangian state space and Eulerian values is conveniently expressed with graph operators that make it possible to conduct all the mathematical operations required by the control process. We show the effectiveness of our approach on classical and more original particle swarm control problems

災害現場におけるマルチロボットの強化学習

首都大学東京, 2019-03-25, 修士（工学）首都大学東