11,131 research outputs found
Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform
In this paper, a hierarchical multi-UAV simulation platform,called XTDrone,
is designed for UAV swarms, which is completely open-source 4 . There are six
layers in XTDrone: communication, simulator,low-level control, high-level
control, coordination, and human interac-tion layers. XTDrone has three
advantages. Firstly, the simulation speedcan be adjusted to match the computer
performance, based on the lock-step mode. Thus, the simulations can be
conducted on a work stationor on a personal laptop, for different purposes.
Secondly, a simplifiedsimulator is also developed which enables quick algorithm
designing sothat the approximated behavior of UAV swarms can be observed
inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the
codes in simulations can be easily transplanted to embeddedsystems. Note that
XTDrone can support various types of multi-UAVmissions, and we provide two
important demos in this paper: one is aground-station-based multi-UAV
cooperative search, and the other is adistributed UAV formation flight,
including consensus-based formationcontrol, task assignment, and obstacle
avoidance.Comment: 12 pages, 10 figures. And for the, see
https://gitee.com/robin_shaun/XTDron
Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms
This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences
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
Role Engine Implementation for a Continuous and Collaborative Multi-Robot System
In situations involving teams of diverse robots, assigning appropriate roles
to each robot and evaluating their performance is crucial. These roles define
the specific characteristics of a robot within a given context. The stream
actions exhibited by a robot based on its assigned role are referred to as the
process role. Our research addresses the depiction of process roles using a
multivariate probabilistic function. The main aim of this study is to develop a
role engine for collaborative multi-robot systems and optimize the behavior of
the robots. The role engine is designed to assign suitable roles to each robot,
generate approximately optimal process roles, update them on time, and identify
instances of robot malfunction or trigger replanning when necessary. The
environment considered is dynamic, involving obstacles and other agents. The
role engine operates hybrid, with central initiation and decentralized action,
and assigns unlabeled roles to agents. We employ the Gaussian Process (GP)
inference method to optimize process roles based on local constraints and
constraints related to other agents. Furthermore, we propose an innovative
approach that utilizes the environment's skeleton to address initialization and
feasibility evaluation challenges. We successfully demonstrated the proposed
approach's feasibility, and efficiency through simulation studies and
real-world experiments involving diverse mobile robots.Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man
and Cybernetics(T-SMC
Decentralized shape formation and force-based interactive formation control in robot swarms
Swarm robotic systems utilize collective behaviour to achieve goals that
might be too complex for a lone entity, but become attainable with localized
communication and collective decision making. In this paper, a behaviour-based
distributed approach to shape formation is proposed. Flocking into strategic
formations is observed in migratory birds and fish to avoid predators and also
for energy conservation. The formation is maintained throughout long periods
without collapsing and is advantageous for communicating within the flock.
Similar behaviour can be deployed in multi-agent systems to enhance
coordination within the swarm. Existing methods for formation control are
either dependent on the size and geometry of the formation or rely on
maintaining the formation with a single reference in the swarm (the leader).
These methods are not resilient to failure and involve a high degree of
deformation upon obstacle encounter before the shape is recovered again. To
improve the performance, artificial force-based interaction amongst the
entities of the swarm to maintain shape integrity while encountering obstacles
is elucidated.Comment: 6 pages, 10 figure
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
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