UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance

Chen, Hongda; Chang, Kuo‐Chu; Agate, Craig S.

doi:10.1109/taes.2013.6494384

Cited by 186 publications

(79 citation statements)

References 30 publications

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“…Implementation In Appendix V six implementations of potential field methods can be seen. Chen et al (2013) implements a vector field method by making use of Lyapunov functions. To determine its performance Chen compares the desired trajectory to the actual trajectory.…”

Section: Cwm Nous Performance In Obstacle Avoidancementioning

confidence: 99%

Performance evaluation in obstacle avoidance

Nous

Meertens

Wagter

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Section: Cwm Nous Performance In Obstacle Avoidancementioning

confidence: 99%

Performance evaluation in obstacle avoidance

Nous

Meertens

Wagter

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The vector field method is used in static or dynamic target tracking [4,5]. The rapidly-exploring random-tree (RRT) method has been applied to the path planning problem of indoor robots and mini UAVs [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Zhang

Liu

et al. 2016

IJGI

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Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.

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“…In recent years several nonlinear control methods, such as the Lyapunov function [1,2], back-stepping [3,4] and nonlinear dynamic inversion [5,6], have been applied in small UAVs flight control in order to improve the attitude and trajectory control performance of UAVs. However, the presented control methods are too complex to design.…”

Section: Introductionmentioning

confidence: 99%

An Integrated System for UAV Control Using a Neural Network Implemented in a Prototyping Board

Artale

Collotta

Milazzo

et al. 2016

J Intell Robot Syst

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Modern aerospace vehicles are expected to have non-conventional flight envelopes and then, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Network (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper a novel real-time control system, based on a NN model, in order to control the trajectories of a hexacopter is proposed. The proposed NN is optimized by the analytical calculation of the embedding parameters. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and computation of the angular velocities of the hexacopter.

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UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance

Cited by 186 publications

References 30 publications

Performance evaluation in obstacle avoidance

Performance evaluation in obstacle avoidance

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

An Integrated System for UAV Control Using a Neural Network Implemented in a Prototyping Board

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