Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

A cooperative active perception approach for swarm robotics

More than half a century after modern robotics first emerged, we still face a landscape in which most of the work done by robots is predetermined, rather than autonomous. A strong understanding of the environment is one of the key factors for autonomy, enabling the robots to make correct decisions based on the environment surrounding them. Classic methods for obtaining robotic controllers are based on manual specification, but become less trivial as the complexity scales. Artificial intelligence methods like evolutionary algorithms were introduced to synthesize robotic controllers by optimizing an artificial neural network to a given fitness function that measures the robots’ performance to solve a predetermined task. In this work, a novel approach to swarm robotics environment perception is studied, with a behavior model based on the cooperative identification of objects that fly around an environment, followed by an action based on the result of the identification process. Controllers are obtained via evolutionary methods. Results show a controller with a high identification and correct decision rates. The work is followed by a study on scaling up that approach to multiple environments. Experiments are done on terrain, marine and aerial environments, as well as on ideal, noisy and hybrid scenarios. In the hybrid scenario, different evolution samples are done in different environments. Results show the way these controllers are able to adapt to each scenario and conclude a hybrid evolution is the best fit to generate a more robust and environment independent controller to solve our task.Mais de um século após a robótica moderna ter surgido, ainda nos deparamos com um cenário onde a maioria do trabalho executado por robôs é pré-determinado, ao invés de autónomo. Uma forte compreensão do ambiente é um dos pontos chave para a autonomia, permitindo aos robôs tomarem decisões corretas baseadas no ambiente que os rodeia. Abordagens mais clássicas para obter controladores de robótica são baseadas na especificação manual, mas tornam-se menos apropriadas à medida que a complexidade aumenta. Métodos de inteligência artificial como algoritmos evolucionários foram introduzidos para obter controladores de robótica através da otimização de uma rede neuronal artificial para uma função de fitness que mede a aptidão dos robôs para resolver uma determinada tarefa. Neste trabalho, é apresentada uma nova abordagem para perceção do ambiente por um enxame de robôs, com um modelo de comportamento baseado na identificação cooperativa de objetos que circulam no ambiente, seguida de uma atuação baseada no resultado da identificação. Os controladores são obtidos através de métodos evolucionários. Os resultados apesentam um controlador com uma alta taxa de identificação e de decisão. Segue-se um estudo sobre o escalonamento da abordagem a múltiplos ambientes. São feitas experiencias num ambiente terrestre, marinho e aéreo, bem como num contexto ideal, ruidoso e híbrido. No contexto híbrido, diferentes samples da evolução ocorrem em diferentes ambientes. Os resultados demonstram a forma como cada controlador se adapta aos restantes ambientes e concluem que a evolução híbrida foi a mais capaz de gerar um controlador robusto e transversal aos diferentes ambientes. Palavras-chave: Robótica evolucionária, Sistemas multi-robô, Cooperação, Perceção, Identificação de objetos, Inteligência artificial, Aprendizagem automática, Redes neuronais, Múltiplos ambientes

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

SwarMAV: A Swarm of Miniature Aerial Vehicles

As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible avenue of development: an autonomous swarm of MAVs which exploits its inherent reliability (through redundancy), and its ability to exchange information among the members, in order to cope with a dynamically changing environment and achieve its mission. We describe the successful realization of a prototype experimental platform weighing only 75g, and outline a strategy for the automatic design of a suitable controller