Fig 4 - uploaded by Min Wang
Content may be subject to copyright.
Source publication
UAV teleoperation is a demanding task, especially for amateur operators who wish to successfully accomplish their mission without collision. In this work we present an integrated 2D LIDAR based Sense-and-Avoid system which actively assists unskilled human operator in obstacle avoidance, so that the operator can focus on higher-level decisions and g...
Contexts in source publication
Context 1
... and v b y,hd , and generates control commands v b x,d and ψ b d for the lower level controller, as shown in Fig. 4. The lower-level controller has been modified so that now it takes in yaw angle in body frame instead of that in inertial frame. The relationship between these two levels of controllers expressed in mathematical form is as follows: Our collision avoidance system consists of three major functionalities, namely, environment perception, ...
Context 2
... the direction of v b avoid will be used to guide the UAV's velocity, while the magnitude of the velocity remains constant. This combination is then used as control input for the controller displayed in Fig. 4, completely overriding the pilot's new control input during the course of collision avoidance. The course of collision avoidance is considered complete when the UAV detects no objects in its zone D and flies at the velocity of the operator's intent, v w intent . We assume that at this point in time the UAV would send a signal to the ...
Citations
... Fernando A. Chicaiza fachicaiza@inaut.unsj.edu.ar 1 In the state of the art, there are several proposals of path planning algorithms to get free-collision paths, such as [13][14][15], which are mainly applied to autonomous robots. On the other hand, force feedback used in bilateral teleoperation presents benefits in improving human operator perception, works including [15][16][17], and [18] show the need for additional support in teleoperation tasks, where visual feedback is not enough to execute the mission satisfactorily. ...
The intervention of a human operator in tasks performed by aerial robots is relevant when the objectives require decisions that an online controller could not take adequately. In this regard, several path planning methods with haptic feedback have been proposed as teleoperation support, and their results have been outstanding in keeping the vehicle collision-free. However, from the teleoperator’s point of view, objective and subjective measurements are relevant to determine the influence that algorithms and data feedback project on the human immersed in the control of the vehicle. This work considers three types of strategies that involve visual feedback support, force feedback, and a three-dimensional path planning algorithm for bilateral teleoperation of aerial vehicles. All strategies are evaluated considering aspects such as success rate of executions, time required to complete the task, average velocity coordination error, kinetic energy of the haptic device, situation of awareness, and mental load. The results obtained show relevant differences between strategies, summarised in a table showing the advantages and drawbacks of one versus the other.
... Some of the most important problems for aerial manipulators are the control and the complexity for some tasks that involve grasping and manipulation maneuvers [1]- [6]. For this reason several efforts have been conducted for robot teleoperation [7], [8]. One of the major problems for teleoperated systems, specially for mobile robots, is the human pilot's visual limited range [9], [10]. ...
... Furthermore, we show the avatar of the aerial robot mimics the dynamic behavior of the aerial manipulator. Also, we test the control law given by (7) and (8). ...
... The first experiment's objective is to measure the error between the setpoint position and the actual position of the vehicle under the action of controls (7) and (8). With the absence and presence of a load. ...
The tasks that an aerial manipulator can perform are incredibly diverse. However, nowadays the technology is not entirely developed to achieve complex tasks autonomously. That is why we propose a human-in-the-loop system that can control a semi-autonomous aerial manipulator to accomplish these kinds of tasks. Furthermore, motivated by the growing trend of virtual reality systems, together with teleoperation, we develop a system composed of: an aerial manipulator model programmed in PX4 and modeled in Gazebo, a virtual reality immersion with an interactive controller, and the interconnection between the systems above via the Internet. This research is the first part of a broader project. In this part, we present experiments in the software in the loop simulation. The code of this work is available on our GitHub page. Also, a video shows the conducted experiments.
... Some of the most important problems for aerial manipulators are the control, and the complexity in achieving a variety of grasping and manipulation tasks [1], [2], [3]. For that, several efforts have been conducted for robot teleoperation , [4] [5]. One of the major problems of teleoperated systems is the difficulties encountered by the human pilot while he/she is performing a task due to its visualization restriction [6]. ...
The tasks that an aerial manipulator can perform are incredibly diverse. However, nowadays the technology is not completely developed to achieve complex tasks autonomously. That's why we propose a human-in-the-loop system that can control a semi-autonomous aerial manipulator to accomplish these kinds of tasks. Furthermore, motivated by the growing trend of virtual reality systems, together with teleoperation, we develop a system composed of: an aerial manipulator model programmed in PX4 and modeled in Gazebo, a virtual reality immersion with an interactive controller, and the interconnection between the aforementioned systems via the Internet. This research is the first part of a broader project. In this part, we present experiments in the software in the loop simulation. The code of this work is liberated on our GitHub page. Also, a video shows the conducted experiments.
