Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

Zhang, Jing; Wu, Jun; Li, Yunsong

doi:10.1177/17298814211042730

Cited by 51 publications

(18 citation statements)

References 25 publications

(42 reference statements)

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“…e length of the path is further shortened, and the number of turning points is also reduced. But the same problem of an unsmooth path at the turning point still exists [23].…”

Section: Step 2 Remove Redundant Jump Pointsmentioning

confidence: 99%

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Zhen

Yang

et al. 2022

Journal of Robotics

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To address the problems of the traditional A ∗ algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A ∗ algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A ∗ algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A ∗ and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.

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“…e length of the path is further shortened, and the number of turning points is also reduced. But the same problem of an unsmooth path at the turning point still exists [23].…”

Section: Step 2 Remove Redundant Jump Pointsmentioning

confidence: 99%

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Zhen

Yang

et al. 2022

Journal of Robotics

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“…Many algorithms enable target search for SAR robots, and these can be broadly classified as traditional algorithms, intelligent algorithms and reinforcement learning algorithms. Traditional algorithms includes:A* [ 13 ], Artificial potential field(APF) [ 14 ]. The basic idea of APF is the target point exerting an "attractive force" on the agent and the obstacle exerting a "repulsive force" on the agent.…”

Section: Introductionmentioning

confidence: 99%

A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm

Hao

Zhao

et al. 2023

PLoS ONE

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Search algorithm plays an important role in the motion planning of the robot, it determines whether the mobile robot complete the task. To solve the search task in complex environments, a fusion algorithm based on the Flower Pollination algorithm and Q-learning is proposed. To improve the accuracy, an improved grid map is used in the section of environment modeling to change the original static grid to a combination of static and dynamic grids. Secondly, a combination of Q-learning and Flower Pollination algorithm is used to complete the initialization of the Q-table and accelerate the efficiency of the search and rescue robot path search. A combination of static and dynamic reward function is proposed for the different situations encountered by the search and rescue robot during the search process, as a way to allow the search and rescue robot to get better different feedback results in each specific situation. The experiments are divided into two parts: typical and improved grid map path planning. Experiments show that the improved grid map can increase the success rate and the FIQL can be used by the search and rescue robot to accomplish the task in a complex environment. Compared with other algorithms, FIQL can reduce the number of iterations, improve the adaptability of the search and rescue robot to complex environments, and have the advantages of short convergence time and small computational effort.

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“…In previous studies, researchers mainly considered using different algorithms to solve the path planning problem of mobile robots without collision: for example, the A* algorithm [ 5 , 6 ], RRT algorithm [ 7 , 8 ], Dijkstra algorithm [ 9 , 10 ], and artificial potential field method (APF) [ 11 , 12 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

An improved beetle antennae search path planning algorithm for vehicles

et al. 2022

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With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path’s straight turns to improve the path’s robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.

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Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

Cited by 51 publications

References 25 publications

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm

An improved beetle antennae search path planning algorithm for vehicles

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