scholarly journals Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3410 ◽  
Author(s):  
Lishan Lin ◽  
Yuji Yang ◽  
Hui Cheng ◽  
Xuechen Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Region Of Interest ◽  
Target Object ◽  
Robotic Arm ◽  
Support Vector ◽  
Control Laws ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
And Control ◽  
High Computational Efficiency

Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.

Modeling and control of a quadrotor tail-sitter unmanned aerial vehicles

2021 ◽  
pp. 095965182110504
Author(s):  
Hongbo Xin ◽  
Yujie Wang ◽  
Xianzhong Gao ◽  
Qingyang Chen ◽  
Bingjie Zhu ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Euler Angle ◽  
Level Flight ◽  
Aerial Vehicles ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Hover Flight ◽  
And Control

The tail-sitter unmanned aerial vehicles have the advantages of multi-rotors and fixed-wing aircrafts, such as vertical takeoff and landing, long endurance and high-speed cruise. These make the tail-sitter unmanned aerial vehicle capable for special tasks in complex environments. In this article, we present the modeling and the control system design for a quadrotor tail-sitter unmanned aerial vehicle whose main structure consists of a traditional quadrotor with four wings fixed on the four rotor arms. The key point of the control system is the transition process between hover flight mode and level flight mode. However, the normal Euler angle representation cannot tackle both of the hover and level flight modes because of the singularity when pitch angle tends to [Formula: see text]. The dual-Euler method using two Euler-angle representations in two body-fixed coordinate frames is presented to couple with this problem, which gives continuous attitude representation throughout the whole flight envelope. The control system is divided into hover and level controllers to adapt to the two different flight modes. The nonlinear dynamic inverse method is employed to realize fuselage rotation and attitude stabilization. In guidance control, the vector field method is used in level flight guidance logic, and the quadrotor guidance method is used in hover flight mode. The framework of the whole system is established by MATLAB and Simulink, and the effectiveness of the guidance and control algorithms are verified by simulation. Finally, the flight test of the prototype shows the feasibility of the whole system.

scholarly journals Design and Analysis of Collaborative Unmanned Surface-Aerial Vehicle Cruise Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Man Zhu ◽  
Yuan-Qiao Wen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Command And Control ◽  
Collaborative Model ◽  
Weighting Method ◽  
Unmanned Surface Vehicle ◽  
Control Networks ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
And Control

With the increasing application of unmanned surface vehicle-unmanned aerial vehicles (USV-UAVs) in maritime supervision, research on their deployment and control is becoming vitally important. We investigate the application of USV-UAVs for synergistic cruising and evaluate the effectiveness of the proposed collaborative model. First, we build a collaborative model consisting of the cruise vehicles and communication, detection, and command-and-control networks for the USV-UAV. Second, based on an analysis of the problems faced by collaborative USV-UAV systems, we establish a model to evaluate the effectiveness of such synergistic cruises. Third, we propose a weighting method for each evaluation factor. Finally, a model consisting of one UAV and four USVs is employed to validate our synergistic cruise model.

Multi-UAV Specification and Control with a Single Pilot-in-the-Loop

2020 ◽  
Vol 08 (04) ◽  
pp. 269-277
Author(s):  
Patricio Moreno ◽  
Santiago Esteva ◽  
Ignacio Mas ◽  
Juan I. Giribet
Keyword(s):  
Remote Control ◽  
Unmanned Aerial Vehicles ◽  
Field Experiments ◽  
Aerial Vehicles ◽  
Simulated Environment ◽  
Aerial Vehicle ◽  
Space Formulation ◽  
Trajectory Following ◽  
And Control ◽  
Multi Uav

This work presents a multi-unmanned aerial vehicle formation implementing a trajectory-following controller based on the cluster-space robot coordination method. The controller is augmented with a feed-forward input from a control station operator. This teleoperation input is generated by means of a remote control, as a simple way of modifying the trajectory or taking over control of the formation during flight. The cluster-space formulation presents a simple specification of the system’s motion and, in this work, the operator benefits from this capability to easily evade obstacles by means of controlling the cluster parameters in real time. The proposed augmented controller is tested in a simulated environment first, and then deployed for outdoor field experiments. Results are shown in different scenarios using a cluster of three autonomous unmanned aerial vehicles.

scholarly journals Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875506 ◽  
Author(s):  
Changxin Huang ◽  
Wei Li ◽  
Chao Xiao ◽  
Binbin Liang ◽  
Songchen Han
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Potential Field ◽  
Region Of Interest ◽  
Field Method ◽  
Attractive Potential ◽  
Straightforward Application ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
The One ◽  
Vehicle Sensors

Persistent surveillance is one of the major tasks envisioned for unmanned aerial vehicles in that some regions of interest need to be continuously surveyed. This distinction from the one-time coverage/exploration does not allow a straightforward application of the most exploration techniques to address the persistent surveillance problem. This article introduces and demonstrates a method of alterable artificial potential field to control a group of unmanned aerial vehicles to stay over the region of interest. We present attractive potential field to drive the unmanned aerial vehicles to desirable areas, obstacle potential field to move away from obstacles, collision avoidance potential field to control unmanned aerial vehicles not to collide with others, and formation of potential field to make unmanned aerial vehicles gather in a group. The simulation results show that the proposed approach could generate collision-free path for unmanned aerial vehicles staying over the region of interest for a long endurance.

scholarly journals Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4991
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Javier Gomez-Avila ◽  
Gehová López-González ◽  
Jorge D. Rios ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Computational Cost ◽  
Support Vector ◽  
Normal Amount ◽  
Human Expert ◽  
Aerial Vehicles ◽  
Training Samples ◽  
Aerial Vehicle ◽  
Water Accumulation ◽  
Low Computational Cost

Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.

The Human Challenges of Command and Control with Multiple Unmanned Aerial Vehicles

2005 ◽  
Vol 49 (1) ◽  
pp. 20-24 ◽  
Author(s):  
Bruce P. Hunn
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Vehicle Operation ◽  
Aerial Vehicle ◽  
Remote Sites ◽  
Unmanned System ◽  
Multiple Unmanned Aerial Vehicles ◽  
And Control ◽  
Direct Feedback ◽  
Display Systems

The unmanned aerial vehicle represents a significant challenge to its operators since they are literally out of touch with the system they control. Operating from remote sites miles from the vehicle they control, they are isolated by space and time from a direct connection to the machine they operate. While the pilot of a manned aircraft can always receive some type of direct feedback from the machine they operate, even if they lose all their control and display systems, they can still perceive many qualities of that machine's system state merely from their senses. However, in contrast, the unmanned system is based solely on an electronic link connecting the operator to their vehicle. This paper will review the historical trends in remote vehicle operation and discuss state-of-the-art in remote control systems as they apply to single or multiple unmanned aerial vehicles.

scholarly journals Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

2019 ◽  
Vol 9 (5) ◽  
pp. 3504
Author(s):  
Aswini N ◽  
Uma S V
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Communication Systems ◽  
Scale Invariant ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Complex Automation ◽  
Novel Method ◽  
Scale Invariant Feature ◽  
And Control

<span lang="EN-US">Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro.</span>

scholarly journals Usage of eye tracker technology in examining attention distribution of operators of unmanned aerial vehicles

2021 ◽  
Vol 33 ◽  
pp. 221-236
Author(s):  
Zoya Hubenova ◽  
Konstantin Metodiev ◽  
Svetla Dimitrova ◽  
Liubomir Alexiev
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Source Code ◽  
Region Of Interest ◽  
Flight Simulator ◽  
Eye Tracker ◽  
Aerial Vehicles ◽  
Spontaneous Movements ◽  
Aerial Vehicle ◽  
Over Time

This article proposes yet another approach towards looking into causes for attention distribution of an operator of unmanned aerial vehicle. During examination, the operator is being tested at dedicated flight simulator while data are gathered and visualized through a mobile eye tracker. Two work stages are considered sequentially, i.e. building a geometric 2D transformation of region of interest (homography) within an image, and overlaying a dynamic heatmap as well. In the former stage, spontaneous movements of the operator’s head, recorded by the video, are eliminated thus enabling the operator to use the mobile eye tracker instead of a desktop-based one. During the latter stage, the distribution of operator’s attention over time is displayed. In order to implement the current research, a source code has been developed in C++ for some features readily available in OpenCV library to be used. In addition, data gathered after carrying out flight session are processed and discussed thoroughly.

A constraint-based approach for planning unmanned aerial vehicle activities

2016 ◽  
Vol 31 (5) ◽  
pp. 486-497
Author(s):  
Christophe Guettier ◽  
François Lucas
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
User Acceptance ◽  
Mission Planning ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Mission Duration ◽  
Aerial Vehicle ◽  
Global Planning ◽  
And Control

AbstractUnmanned Aerial Vehicles (UAV) represent a major advantage in defense, disaster relief and first responder applications. UAV may provide valuable information on the environment if their Command and Control (C2) is shared by different operators. In a C2 networking system, any operator may request and use the UAV to perform a remote sensing operation. These requests have to be scheduled in time and a consistent navigation plan must be defined for the UAV. Moreover, maximizing UAV utilization is a key challenge for user acceptance and operational efficiency. The global planning problem is constrained by the environment, targets to observe, user availability, mission duration and on-board resources. This problem follows previous research works on automatic mission Planning & Scheduling for defense applications. The paper presents a full constraint-based approach to simultaneously satisfy observation requests, and resolve navigation plans.

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