UAV online path planning technology based on deep reinforcement learning

Fan, Jiaxuan; Wang, Zhenya; Ren, Jinlei; Lü, Ying; Liu, Yiheng

doi:10.1109/cac51589.2020.9327752

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…On the other hand, for different task backgrounds and environments, suitable reward functions can be designed as incentive signals for drone decision-making, helping drones to complete reconnaissance decision-making tasks autonomously through training. Zhao Yu et al used deep reinforcement learning algorithms to construct a control model and a coordination mechanism between drones to control the collaborative flight of multiple fixed wing drones, and verified the effectiveness of collision avoidance during collaborative flight of multiple fixed wing drones; Fan Longtao et al proposed a reinforcement learning method based on attention mechanism [32,33]. Firstly, a task allocation solution model was constructed through attention mechanism, and then the model was continuously optimized using reinforcement algorithm to obtain an approximate optimal solution.…”

Section: Collaborative Trajectory Planning Technologymentioning

confidence: 99%

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

Huang,

Yang,

et al. 2024

J. Intell. Commun.

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This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

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Section: Collaborative Trajectory Planning Technologymentioning

confidence: 99%

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

Huang,

Yang,

et al. 2024

J. Intell. Commun.

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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%

“…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. The authors in [15,16] adopt the deep reinforcement learning (DRL) algorithm to optimize the reaction coefficients, which retains the advantages of the analytical method and maintains a high calculation speed. The algorithm has great application potential.…”

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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“…Several online path planners that are commonly used include Neural Network (NN), Stochastic models, Probabilistic models and Graph. NN-based approaches are choosen due to their hardware advancement and big data availability such as an improved Q-learning based method for reactive obstacle avoidance [13], a deep reinforcement learning approach in assisted edge computing networks [14], and a deep reinforcement learning technique to calculate online path planning [15]. Those papers claimed that generated online path is smooth, fast and fuel efficient.…”

Section: Introductionmentioning

confidence: 99%

Online Path Planning Framework for UAV in Rural Areas

Airlangga

Liu

2022

IEEE Access

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A motion strategy plays an important role in supporting autonomous Unmanned Aerial Vehicle (UAV) movement. Many studies have been conducted to improve the motion frameworks in terms of its robustness, safety and performance. Most of them worked on the prior known maps scenario where the area information was collected by Global Positioning System (GPS) and satellite cameras. Even though the scheme can provide high quality map, the computation of motion planning remains dependent on the communication signal. In the rural areas such as forests and mountains, where communication signal does not perform well, unclear and noisy terrain maps can be generated and lead to mission failure. Therefore, it is significant that an alternative framework to enhance autonomous UAV motion performance in these certain conditions should be developed. Our work focuses on developing a high performance path planner for autonomous UAV motion when communication signal does not work well in rural areas. The search mission problem in forest terrain has been implemented in 3D simulation as an evaluation. By conducting a simulation process repeatedly with different test cases for positions, time constraints, flight speed (3-11 m/s) and flight range, our path planning framework can achieve completeness between 90-100% and better performance compared to others.INDEX TERMS Online path planning, motion planning, UAV, rural areas.

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UAV online path planning technology based on deep reinforcement learning

Cited by 5 publications

References 8 publications

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

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

Online Path Planning Framework for UAV in Rural Areas

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