358 research outputs found
Multi-Robot Systems: Challenges, Trends and Applications
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
Tag Recognition for Quadcopter Drone Movement
Unmanned Aerial Vehicle (UAV) drone such as Parrot AR.Drone 2.0 is a flying mobile robot which has been popularly researched for the application of search and rescue mission. In this project, Robot Operating System (ROS), a free open source platform for developing robot control software is used to develop a tag recognition program for drone movement. ROS is popular with mobile robotics application development because sensors data transmission for robot control system analysis will be very handy with the use of ROS nodes and packages once the installation and compilation is done correctly. It is expected that the drone can communicate with a laptop via ROS nodes for sensors data transmission which will be further analyzed and processed for the close-loop control system. The developed program consisting of several packages is aimed to demonstrate the recognition of different tags by the drone which will be transformed into a movement command with respect to the tag recognized; in other words, a visual-based navigation program is developed
Swarms of Unmanned Aerial Vehicles – A Survey
The purpose of this study is to focus on the analysis
of the core characteristics of swarms of drones or Unmanned Aerial Vehicles and
to present them in a way that facilitates analysis of public awareness on such
swarms. Furthermore, the functionality, problems, and importance of drones are
highlighted. Lastly, the experimental survey from a bunch of academic population demonstrates that the swarms of drones
are fundamental future agendas and will be adapted
by the time.</p
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
SwarmLab: a Matlab Drone Swarm Simulator
Among the available solutions for drone swarm simulations, we identified a
gap in simulation frameworks that allow easy algorithms prototyping, tuning,
debugging and performance analysis, and do not require the user to interface
with multiple programming languages. We present SwarmLab, a software entirely
written in Matlab, that aims at the creation of standardized processes and
metrics to quantify the performance and robustness of swarm algorithms, and in
particular, it focuses on drones. We showcase the functionalities of SwarmLab
by comparing two state-of-the-art algorithms for the navigation of aerial
swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze
the variability of the inter-agent distances and agents' speeds during flight.
We also study some of the performance metrics presented, i.e. order, inter and
extra-agent safety, union, and connectivity. While Olfati-Saber's approach
results in a faster crossing of the obstacle field, Vasarhelyi's approach
allows the agents to fly smoother trajectories, without oscillations. We
believe that SwarmLab is relevant for both the biological and robotics research
communities, and for education, since it allows fast algorithm development, the
automatic collection of simulated data, the systematic analysis of swarming
behaviors with performance metrics inherited from the state of the art.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Quadcopter drone formation control via onboard visual perception
Quadcopter drone formation control is an important capability for fields like area surveillance, search and rescue, agriculture, and reconnaissance. Of particular interest is formation control in environments where radio communications and/or GPS may be either denied or not sufficiently accurate for the desired application.
To address this, we focus on vision as the sensing modality. We train an Hourglass Convolutional Neural Network (CNN) to discriminate between quadcopter pixels and non-quadcopter pixels in a live video feed and use it to guide a formation of quadcopters. The CNN outputs "heatmaps" - pixel-by-pixel likelihood estimates of the presence of a quadcopter. These heatmaps suffer from short-lived false detections. To mitigate these, we apply a version of the Siamese networks technique on consecutive frames for clutter mitigation and to promote temporal smoothness in the heatmaps. The heatmaps give an estimate of the range and bearing to the other quadcopter(s), which we use to calculate flight control commands and maintain the desired formation.
We implement the algorithm on a single-board computer (ODROID XU4) with a standard webcam mounted to a quadcopter drone. Flight tests in a motion capture volume demonstrate successful formation control with two quadcopters in a leader-follower setup
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