UAV autonomous collision avoidance approach

He, Ran; Wei, Ruixuan; Zhang, Qirui

doi:10.1080/00051144.2017.1388646

Cited by 23 publications

(9 citation statements)

References 27 publications

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“…X, X o = ‖ ‖ X o − X ‖ ‖ denotes a vector and the magnitude of it denotes the Euclidean distance between the UAV and obstacle. To reduce the unnecessary calculations of the UAV obstacle avoidance and ensure the safety of UAV, the collision avoidance boundary should be set and 0 represents the safety distance [18].…”

Section: Apf Collision Avoidance Algorithmmentioning

confidence: 99%

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Liu

Chi

et al. 2020

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In recent years, multiple Unmanned Aerial Vehicle (UAV) formation flight has attracted worldwide research interest, for its potential benefits of scalability and flexibility. In complex urban environments, the successful operation of those UAVs requires the system to provide certain safety level. As one of the key requirements, collision avoidance improves the system's ability to accommodate operational environment variations, and to perform multiple tasks. To achieve this, artificial potential field (APF) has been recognized as one of the most suitable methods along with drone control. Although there has been substantial relevant work on the APF for single UAV in static environment, more efforts are desired to address formation maneuvers in complex environments such as urban. Most importantly, the traditional APF algorithms do not account for random errors in navigation solutions, which can bring potential risk to the UAV system. In response, this paper proposes a new APF algorithm that employs navigation information in complex urban environments, and the goal is to realize UAV formation collision avoidance. By augmenting the APF algorithm with UAV navigation information, the potential risk caused by navigation uncertainty can be mitigated, especially in the Global Navigation Satellite System (GNSS) challenged environment. The principle of the new APF approach is adaptively estimating the parameters of potential field force function, using the variance of navigation information and user-defined confidence probability. This new approach is applied in the synchronized UAV formation collision avoidance control. As a result, the UAVs can achieve fast position and attitude adjustment with high safety confidence. To verify the algorithm, quadrotors with emulated GNSS receivers are used to generate observation data. These data are incorporated into a complex urban environment simulation, where multiple sets of virtual obstacles are injected. Results show that the proposed method can achieve safe and efficient collision avoidance for cooperative formation flight in urban GNSS challenged environment.

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Section: Apf Collision Avoidance Algorithmmentioning

confidence: 99%

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Liu

Chi

et al. 2020

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“…While obstacle avoidance with known information could be seamlessly realized, those with scarce information about any obstacles face various limitations. Some of the obstacle avoidance algorithms are bug algorithms, potential field algorithms, vector field algorithms, genetic algorithms, fuzzy logic algorithms, and neural network algorithms [5], [6], [7], [8], [9], [10], [11]. The bug algorithm, the potential field algorithm, and the vector field algorithm are called traditional obstacle avoidance algorithm methods.…”

Section: Introductionmentioning

confidence: 99%

“…The core of the fuzzy logic method is the fuzzy controller. In the fuzzy logic method, as the number of obstacles increases, the amount of calculation is large [10]. The neural network is a mathematical or computational model that mimics the structure and function of a biological neural network.…”

Section: Introductionmentioning

confidence: 99%

A Software System for Planning Collision-Free Motion for Multi-Agent Systems Using Control Barrier Functions

SIRI,

BAI,

SVININ

2024

Preprint

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The research concerning the control of multi-agent systems has grown rapidly over the past decade, attributed to their adaptiveness, efficiency, and robustness in dynamic environments. While multi-agent systems have a vast potential, safety, specifically obstacle avoidance, is indispensable when controlling such systems. In this paper, a control barrier function-based algorithm is employed to deal with obstacle avoidance problems. The computational load of the proposed algorithm remains constant even as the scale of multi-agent systems increases, making it highly compatible. Unlike conventional obstacle avoidance algorithms, the proposed algorithm guarantees a collision-free motion of agents. The feasibility of the proposed algorithm is verified under both simulations and experiments.

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“…PD or PID -controllers [3,5,[8][9][10][11][12][13], linear-quadratic controller [1,2], the model predictive control [3,7], Backstepping Control [5,6], Sliding Mode Control [5] and Inverse Control [2,5,14], using neural networks [24] are the most popular controllers used for quadcopter control. There are also some adaptive algorithms for controlling a quadcopter [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Development of Adaptive Control for an Asymmetric Quadcopter

Beniak¹,

Gudzenko²

2020

PAR

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The paper presents an adaptive control algorithm for an asymmetric quadcopter. For determining the control algorithm, the identification was made, and an identification algorithm is presented in the form of a recursive method. The control method is realized using inverse dynamics, full state feedback and finally adaptive control method. The algorithms for the off-line and on-line identification of quadcopter model parameters are also presented. The paper shows the effectiveness of the selected algorithm on the example of the movement along a given trajectory. Finally, recommendations of the application of these different methods are made.

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UAV autonomous collision avoidance approach

Cited by 23 publications

References 27 publications

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

A Software System for Planning Collision-Free Motion for Multi-Agent Systems Using Control Barrier Functions

Development of Adaptive Control for an Asymmetric Quadcopter

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