814 research outputs found
Dynamic Landing of an Autonomous Quadrotor on a Moving Platform in Turbulent Wind Conditions
Autonomous landing on a moving platform presents unique challenges for
multirotor vehicles, including the need to accurately localize the platform,
fast trajectory planning, and precise/robust control. Previous works studied
this problem but most lack explicit consideration of the wind disturbance,
which typically leads to slow descents onto the platform. This work presents a
fully autonomous vision-based system that addresses these limitations by
tightly coupling the localization, planning, and control, thereby enabling fast
and accurate landing on a moving platform. The platform's position,
orientation, and velocity are estimated by an extended Kalman filter using
simulated GPS measurements when the quadrotor-platform distance is large, and
by a visual fiducial system when the platform is nearby. The landing trajectory
is computed online using receding horizon control and is followed by a boundary
layer sliding controller that provides tracking performance guarantees in the
presence of unknown, but bounded, disturbances. To improve the performance, the
characteristics of the turbulent conditions are accounted for in the
controller. The landing trajectory is fast, direct, and does not require
hovering over the platform, as is typical of most state-of-the-art approaches.
Simulations and hardware experiments are presented to validate the robustness
of the approach.Comment: 7 pages, 8 figures, ICRA2020 accepted pape
Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles
Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications. The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment
Visual Servoing Approach for Autonomous UAV Landing on a Moving Vehicle
We present a method to autonomously land an Unmanned Aerial Vehicle on a
moving vehicle with a circular (or elliptical) pattern on the top. A visual
servoing controller approaches the ground vehicle using velocity commands
calculated directly in image space. The control laws generate velocity commands
in all three dimensions, eliminating the need for a separate height controller.
The method has shown the ability to approach and land on the moving deck in
simulation, indoor and outdoor environments, and compared to the other
available methods, it has provided the fastest landing approach. It does not
rely on additional external setup, such as RTK, motion capture system, ground
station, offboard processing, or communication with the vehicle, and it
requires only a minimal set of hardware and localization sensors. The videos
and source codes can be accessed from http://theairlab.org/landing-on-vehicle.Comment: 24 page
Mixed marker-based/marker-less visual odometry system for mobile robots
When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test
An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets
The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592
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