256 research outputs found

    Distributed Swarm Formation Using Mobile Agents

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    This chapter presents decentralized control algorithms for composing formations of swarm robots. The robots are connected by communication networks. They initially do not have control program to compose formations. Control programs that implement our algorithm are introduced later from outside as mobile software agents. Our controlling algorithm is based on the pheromone communication of social insects such as ants. We have implemented the ant and the pheromone as mobile software agents. Ant agents control the robots. Each ant agent has partial information about the formation it is supposed to compose. The partial information consists of relative locations with neighbor robots that are cooperatively composing the formation. Once the ant agent detects an idle robot, it occupies that robot and generates the pheromone agent to attract other ant agents to the location for neighbor robots. Then the pheromone agent repeatedly migrates to other robots to diffuse attracting information. Once the pheromone agent reaches the robot with an ant agent, the ant agent migrates to the robot closest to the location pointed by the pheromone agent and then drives the robot to the location. We have implemented simulators based on our algorithm and conducted experiments to demonstrate the feasibility of our approach

    Adaptive and learning-based formation control of swarm robots

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    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

    BEHAVIORAL COMPOSITION FOR HETEROGENEOUS SWARMS

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    Research into swarm robotics has produced a robust library of swarm behaviors that excel at defined tasks such as flocking and area search, many of which have potential for application to a wide range of military problems. However, to be successfully applied to an operational environment, swarms must be flexible enough to achieve a wide array of specific objectives and usable enough to be configured and employed by lay operators. This research explored the use of the Mission-based Architecture for Swarm Composability (MASC) to develop mission-specific tactics as compositions of more general, reusable plays for use with the Advanced Robotic Systems Engineering Laboratory (ARSENL) swarm system. Three tactics were developed to conduct autonomous search of a geographic area and investigation of generated contacts of interest. The tactics were tested in live-flight and virtual environment experiments and compared to a preexisting monolithic behavior implementation completing the same task. Measures of performance were defined and observed that verified the effectiveness of solutions and confirmed the advantages that composition provides with respect to reusability and rapid development of increasingly complex behaviors.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

    Recent Advances in Multi Robot Systems

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    To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

    Connectivity Optimization in Robotic Networks

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    La collaboration entre multiple appareils électroniques (e.g. smartphones, ordinateurs, robots, senseurs et routeurs) est une tendance qui suscite un vif intérêt tant ses applications semblent prometteuses. Les maisons autonomes ou villes intelligentes figurent parmi la prodigieuse variété d’exemples. La communication entre appareils est une des clés du succès de leur coopération. Sans un bon système de communication, les appareils se retrouvent vite incapables d’échanger l’information nécessaire à la prise de décision. Pour garantir une bonne communication, il faut un réseau solide sur lequel elle puisse reposer. Nous pourrions envisager une organisation centralisée, puisqu’elles sont si répandues. Nos téléphones portables communiquent grâce à des antennes-relais ; et nous naviguons sur l’internet grâce à des routeurs. Dans un réseau centralisé, si un noeud principal, tel qu’une antenne ou un routeur, est défaillant, la capacité à communiquer en est dramatiquement diminuée. Or, certaines collaborations entre appareils interviennent, parfois, dans des situations où les infrastructures classiques ne sont pas accessibles. C’est le cas pour les opérations de sauvetages, où les moyens de communications classiques ont pu être endommagés à la suite d’un sinistre. D’autres organisations sont alors plus judicieuses. Dans les réseaux ad hoc, par exemple, il n’existe pas de noeud central, car chaque appareil peut servir au transit de l’information. Cette dissertation s’intéresse à la mise en place de réseaux ad hoc et mobiles entre smartphones et drones. Elle s’inscrit dans le cadre d’un partenariat, entre Humanitas Solutions et l’École Polytechnique de Montréal, qui vise à établir un moyen de communication basé sur ces appareils, pour connecter victimes et premiers secours lors d’opérations de sauvetages. Pour mener à bien ce projet, nous devons permettre aux appareils électroniques de communiquer sans recourir à quelconque infrastructure. Pour relayer l’information, nous devons également maintenir les drones connectés au-dessus de la zone sinistrée.----------ABSTRACT: Because of their promising applications, the interest for machine-to-machine interaction has soared (e.g. between smartphones, laptops, robots, sensors, or routers). Autonomous homes and smart cities are just two examples among the many. Without a good communication system, devices are unable to share relevant information and take effective decisions. Thus, inter-device communication is key for successful cooperations. To guarantee suitable communication, devices need to rely on a robust network. One might think of classical centralized network architecture since it is so common – antennae relay our smartphone communications, and routers provide us with an Internet connection at home. However, this architecture is not adequate for every application. When a central node (e.g. an antenna or a router) fails, it can cripple all the network. Moreover, fixed infrastructure is not always available, which is detrimental for applications like search and rescue operations. Hence, other network designs can be more suitable, like ad hoc networks, where there is no central node and every device can route information. This work aims at establishing mobile ad hoc networks between multiple devices for search and rescue operations. This thesis is framed by a partnership between Humanitas Solutions and École Polytechnique de Montréal, whose goal is to relay information between victims and first responders by the use of smartphones and flying robots (i.e. drones). For this purpose, we have to enable infrastructureless communications between devices and maintain drones connected over the disaster area

    Mobile Robots

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    The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

    Formation control of autonomous vehicles with emotion assessment

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    Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks

    Multi-Robot Systems: Challenges, Trends and Applications

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    This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
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