UAV Obstacle Avoidance Algorithm Based on Ellipsoid Geometry

Sasongko, Rianto Adhy; Rawikara, Seno Sahisnu; Tampubolon, Hansel J.

doi:10.1007/s10846-017-0543-4

Cited by 25 publications

(12 citation statements)

References 9 publications

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“…In Ref. 32, an OA algorithm is proposed that locates and designs paths via ellipses that encircle obstacles in the path. Fiorini and Shiller (33) introduced the concept of For calculating the desired distance between all agents except the leader:…”

Section: B Geometric Approachmentioning

confidence: 99%

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

2020

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A formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

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Section: B Geometric Approachmentioning

confidence: 99%

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

2020

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“…Particle swarm optimization fuzzy control method [10,11], artificial potential tangent vector method [12], improved APF method based on case reasoning [13] and other applications in path planning of mobile robots provide experience for UAV path planning. The ellipsoid is used as the restricted area of the obstacle, and the geometric characteristics of the ellipsoid are used to search the path of obstacle avoidance, so as to realize the obstacle avoidance of UAV [14]. The obstacle avoidance beetle antenna search (OABAS) algorithm is a new path planning algorithm based on the biological heuristic algorithm, which is applied to the global path planning of UAV to achieve obstacle avoidance [15].…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Sun

Huang

et al. 2020

IEEE Access

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In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.INDEX TERMS Cluster control, virtual core algorithm, cluster reconstruction, track planning, improved APF

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“…In most of the missions, it is essential for autonomous robots to detect and avoid various obstacles while maneuver in unknown cluttered environments safely. Numerous methods have been proposed and adapted successfully to different robots [ 2 , 3 , 4 , 5 ]. However, conventional methods may impose intensive computational demand [ 6 , 7 ] and are often built upon a set of assumptions that are likely not to be satisfied in practice [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Learn to Steer through Deep Reinforcement Learning

Esfahani

Yuan

et al. 2018

Sensors

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It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-end deep reinforcement learning algorithm in this paper to improve the performance of autonomous steering in complex environments. By embedding a branching noisy dueling architecture, the proposed model is capable of deriving steering commands directly from raw depth images with high efficiency. Specifically, our learning-based approach extracts the feature representation from depth inputs through convolutional neural networks and maps it to both linear and angular velocity commands simultaneously through different streams of the network. Moreover, the training framework is also meticulously designed to improve the learning efficiency and effectiveness. It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images. The proposed method is evaluated and compared with a series of baseline methods in various virtual environments. Experimental results demonstrate the superiority of the proposed model in terms of average reward, learning efficiency, success rate as well as computational time. Moreover, a variety of real-world experiments are also conducted which reveal the high adaptability of our model to both static and dynamic obstacle-cluttered environments.

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UAV Obstacle Avoidance Algorithm Based on Ellipsoid Geometry

Cited by 25 publications

References 9 publications

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Learn to Steer through Deep Reinforcement Learning

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