UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment

Feng, Jianxin; Zhang, Jingze; Zhang, Geng; Xie, Shuang; Ding, Yuanming; Liu, Zhiguo

doi:10.1109/access.2021.3128295

Cited by 16 publications

(19 citation statements)

References 27 publications

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“…A dynamic path planning algorithm based on obstacles’ position prediction and modified APF—HOAP proposed by Feng et al, could effectively deal with the influence of dynamic obstacles. However, the algorithm did not consider the motion characteristics of fixed-wing UAVs, its planning results permitting UAVs to avoid obstacles by hovering first before making a detour 19 . Bai et al gridded the obstacles and then combined the DWA algorithm with the improved A* algorithm to plan shorter and smoother paths and deal with dynamic obstacles 38 .…”

Section: Related Workmentioning

confidence: 99%

“…When the problem is extended from single UAV path planning to cooperative UAV-formation path planning, the additional constraints that need to be considered limit the planning methods based on the global search algorithm enormously, as these algorithms have difficulty coping with dynamic mission scenarios. Although it can be difficult to maintain the most favorable flight path obtained using online search algorithms, they can effectively deal with UAV formation planning in the context of dynamically changing targets and obstacles 19 . Among them, the artificial potential field (APF) method obtains the track by simulating the attraction from a target and the repulsive force from an obstacle, so that it can plan the track in a three-dimensional space, maintain good performance, and deal with dynamic targets and obstacles 20 – 22 .…”

Section: Introductionmentioning

confidence: 99%

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Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Fang

Yao

Zhu

et al. 2023

Sci Rep

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The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Fang

Yao

Zhu

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The route comes into touch with impediments at the vertices and the edges, which might lead to possible accidents [23]. Time-consuming and lacking in flexibility [25].…”

Section: Visibility Graphmentioning

confidence: 99%

“…Best implemented in an immobile setting with stationary barriers. Local minimum of potential may be trapped by robots [25].…”

Section: Potential Fieldmentioning

confidence: 99%

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Abu

Bukhari²,

Adli³

et al. 2022

Journal of Robotics and Control

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Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot.

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“…Traditional UAV path planning algorithms include the artificial potential field method, heuristic algorithm, ant colony algorithm, etc. The UAV path planning method using an improved artificial potential field was proposed in [ 6 , 7 ], which effectively solved the path planning of multi-UAS and UAV obstacle avoidance against dynamic obstacles by introducing a rotating potential field and Markov prediction model. In [ 8 , 9 , 10 ], an improved heuristic algorithm was proposed to solve the problem of UAV path planning and mission area coverage in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

Zhao¹,

Gan²,

Liang³

et al. 2022

Entropy

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Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

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UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment

Cited by 16 publications

References 27 publications

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

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