Obstacle Avoidance of UAV Based on Neural Networks and Interfered Fluid Dynamical System

Wang, Yanxiang; Wang, Honglun; Wen, Jiayun; Lun, Yuebin; Wu, Jianfa

doi:10.1109/icus50048.2020.9274988

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…Among novel path planning techniques, the Interfered Fluid Dynamical System (IFDS) method 16,17 appears to have the highest practicality in terms of low-cost computation, real-time execution, 3D complex environments handleability, and dynamic obstacle avoidance. The success of the hybrid IFDS algorithm in addressing these challenges has been well-documented in various literature [18][19][20][21][22] . The versatility of the IFDS algorithm has been demonstrated across different applications.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Another significant application involves using the IFDS as a foundation for a multi-agent formation (MAF) framework 24 . Additionally, a study by Wu, J. et al 25 refined the manoeuvre control for obstacle avoidance (MCOA) of fixed-wing UAVs by implementing a deep-reinforcement-learning-based reactive online decision-making 19 have proposed the use of a Neural Network to adjust the IFDS parameters adaptively according to the environmental information. Lastly, various reinforcement learning algorithms can be applied to an adaptive interfered fluid dynamic system algorithm (AIFDS) 22 .…”

Section: Problem Formulationmentioning

confidence: 99%

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Rerouting UAVs in Dynamic Environment Based on Optimised Interfered Fluid Dynamical System

Tamanakijprasart,

Mondal,

Tsourdos

2024

Preprint

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Navigating unmanned aerial vehicles (UAVs) through areas full of obstacles is a complex task. Traditional path planning methods, which rely on pre-generated paths, fall short in real-world scenarios where dynamic obstacles are introduced. To ensure mission success, UAVs must reroute dynamically to avoid collisions. The need for real-time decision-making further complicates this dynamic path-planning problem. We have developed a novel dynamic path-planning algorithm to address these challenges based on the Interfered Fluid Dynamical System (IFDS) framework. Drawing inspiration from how water naturally flows around obstacles, the IFDS algorithm adapts to changing environments to generate paths. We have augmented IFDS with environmental data, such as weather and terrain, to enhance its path calculations and optimise it using numerical methods. Every aspect of the algorithm is expressed in a closed form, enabling efficient real-time execution even in a three-dimensional environment, making our solution particularly suited for real-world UAV navigation.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Rerouting UAVs in Dynamic Environment Based on Optimised Interfered Fluid Dynamical System

Tamanakijprasart,

Mondal,

Tsourdos

2024

Preprint

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“…As shown in Figure 3, the combination of different coefficients can determine the shape and direction of the path. In previous researches [14][15][16], receding horizon control (RHC) strategy was mostly used to optimize these coefficients online. However, the serial solution mechanism of RHC cannot well meet the real-time requirements in complex radio environments.…”

Section: Problem Formulationmentioning

confidence: 99%

“…And it is also necessary to optimize the reaction coefficients of the IFDS model to get the best path for the UAV which is surrounded by obstacles so that the UAV flight path is the shortest. In [14], the neural network is used to optimize the reaction coefficients of the IFDS model. The relative positions between UAV, the destination and obstacles are extracted from the sample data as the input of the neural network, and the reaction coefficient of the IFDS model is used as the output of the neural network.…”

Section: Introductionmentioning

confidence: 99%

A 3D REM-Guided UAV Path Planning Method under Communication Connectivity Constraints

Liu

Zhou

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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With the emergence of a large number of smart devices, the radio environment in which unmanned aerial vehicles (UAVs) take tasks is becoming more and more complex, which puts forward higher requirements for UAVs’ situational awareness and autonomous obstacle avoidance capabilities. To tackle this issue, we propose a three-dimension (3D) UAV path planning method under communication connectivity constraints guided by radio environment maps (REMs), which are distributed by ground edge servers in the form of compressed global REMs and detailed local REMs. An interfered fluid dynamic system (IFDS) model is deployed on UAVs to allow them to avoid obstacles and plan paths. We propose a twin-delayed deep deterministic policy gradient- (TD3-) based deep reinforcement learning (DRL) method to optimize the reaction coefficients of UAVs to avoid obstacles and improve the signal to interference plus noise ratio (SINR). The simulation results show that the proposed algorithm can effectively avoid static obstacles and dynamic interference under communication connectivity constraints, significantly improve the communication stability with a higher receive signal SINR and reduce the cost of UAV performing tasks with the shortest path.

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“…Compared with some other swarm intelligence algorithms, the SSA has stronger optimization capabilities and faster search efficiency, good stability, and strong robustness. It has been used in UAV trajectory planning [25] and image segmentation [26]. When the algorithm search is close to the global optimum, there will be problems, such as reduced population diversity and ease of falling into local optimum.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Yang

Liu

et al. 2021

Applied Sciences

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Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.

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Obstacle Avoidance of UAV Based on Neural Networks and Interfered Fluid Dynamical System

Cited by 15 publications

References 14 publications

Rerouting UAVs in Dynamic Environment Based on Optimised Interfered Fluid Dynamical System

Rerouting UAVs in Dynamic Environment Based on Optimised Interfered Fluid Dynamical System

A 3D REM-Guided UAV Path Planning Method under Communication Connectivity Constraints

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

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