6,077 research outputs found
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
This paper proposes a novel centralized training and distributed execution
(CTDE)-based multi-agent deep reinforcement learning (MADRL) method for
multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access
applications. For the purpose, a single neural network is utilized in
centralized training for cooperation among multiple agents while maximizing the
total quality of service (QoS) in mobile access applications.Comment: 2 pages, 4 figure
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
AltURI: a thin middleware for simulated robot vision applications
Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use
Mathematical modeling and vertical flight control of a tilt-wing UAV
This paper presents a mathematical model and vertical flight control algorithms for a new tilt-wing unmanned aerial vehicle (UAV). The vehicle is capable of vertical take-off and landing (VTOL). Due to its tilt-wing structure, it can also fly horizontally. The mathematical model of the vehicle is obtained using
Newton-Euler formulation. A gravity compensated PID controller is designed for altitude control, and three PID controllers are designed for attitude stabilization of the vehicle. Performances of these controllers are
found to be quite satisfactory as demonstrated by indoor and outdoor flight experiments
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