A Self-contained Teleoperated Quadrotor: On-Board State-Estimation and Indoor Obstacle Avoidance

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 global objectives in UAV applications such as search and rescue, farming etc. Specifically, with our perception-assistive vehicle control design, novel adaptive virtual cushion force field (AVCFF) based avoidance strategy, and integrated sensing solution, the proposed UAV teleoperation assistance system is capable of obstacle detection and tracking, as well as automatic avoidance in complex environment where both static and dynamic objects are present. The proposed system is constructed on the basis of Hector Quadrotor open source framework [1], and its effectiveness is demonstrated and validated on a realistic simulated UAV platform in Gazebo simulations where the UAV is operated at a high speed.

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“…Experiments are conducted in a static environment with a single object. [11] is an improvement of [8] with added functionalities.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Teleoperation Assistance System for Collision Avoidance of High-speed UAVs in Complex Environments

Wang

Voos

2020

2020 17th International Conference on Ubiquitous Robots (UR)

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“…Some simulation results show that the proposed method can help the robot avoid an obstacle without changing the initial path. In [19], we have a four-dimensional unmanned aerial vehicle's platform equipped with a miniature computer equipped with a set of small sensors for this work. The platform is capable of accurately estimating the precision mode, tracking the speed of the user, and providing uninterrupted navigation.…”

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Rostami

Sangaiah

Wang

et al. 2019

J Wireless Com Network

162

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In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

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“…In recent years, aerial tracking has received considerable attention because of its wide applications such as indoor obstacle avoidance [1], disaster response [2], and mounting sensors [3]. The objective of aerial tracking is to predict the location of the object in the following frames based on its initial state.…”

Section: Introductionmentioning

confidence: 99%

Siamese Anchor Proposal Network for High-Speed Aerial Tracking

Cao

et al. 2020

Preprint

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In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency, thereby impeding their real-world deployment on mobile platforms like the unmanned aerial vehicle (UAV). In this work, a novel two-stage siamese network-based method is proposed for aerial tracking, i.e., stage-1 for high-quality anchor proposal generation, stage-2 for refining the anchor proposal. Different from anchor-based methods with numerous pre-defined fixed-sized anchors, our no-prior method can 1) make tracker robust and general to different objects with various sizes, especially to small, occluded, and fast-moving objects, under complex scenarios in light of the adaptive anchor generation, 2) make calculation feasible due to the substantial decrease of anchor numbers. In addition, compared to anchorfree methods, our framework has better performance owing to refinement at stage-2. Comprehensive experiments on three benchmarks have proven the state-of-the-art performance of our approach, with a speed of ∼200 frames/s.

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A Self-contained Teleoperated Quadrotor: On-Board State-Estimation and Indoor Obstacle Avoidance

Cited by 11 publications

References 24 publications

An Integrated Teleoperation Assistance System for Collision Avoidance of High-speed UAVs in Complex Environments

An Integrated Teleoperation Assistance System for Collision Avoidance of High-speed UAVs in Complex Environments

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Siamese Anchor Proposal Network for High-Speed Aerial Tracking

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