A-star algorithm for expanding the number of search directions in path planning

Zhang, Zailong; Wang, Shanyu; Zhou, Jianwei

doi:10.1109/ainit54228.2021.00049

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…The A* algorithm [13,14] is a method for quickly searching for the shortest path. Due to its simple operation and good results, it is widely used in path planning.…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Fan,

Li,

Chen

et al. 2024

Drones

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To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

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“…The A* algorithm [13,14] is a method for quickly searching for the shortest path. Due to its simple operation and good results, it is widely used in path planning.…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Fan,

Li,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

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“…These limitations include constraints on the search space size, challenges in selecting appropriate heuristic functions, and concerns regarding completeness and optimality guarantees. Additionally, traditional A* algorithm may encounter issues such as an excessive number of turning points, lack of path smoothness, and longer path distances [3].…”

Section: Introductionmentioning

confidence: 99%

Modified A* algorithm for path smoothing and obstacle avoidance

Geng

2024

ACE

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With the rapid development of robotics technology, path planning is a crucial aspect of autonomous robot systems. Among them, planning paths involves using the A* algorithm, which is a common method. However, traditional A* algorithm has several limitations in path planning, such as poor real-time performance, large amount of computation per node, long computation time, low algorithmic search efficiency. Based on this, two improved approaches for the A* algorithm are proposed. The first is expanding the obstacles in the map by increasing their expansion radius. The second is the Hybrid A* algorithm, which optimizes the A* algorithm by modifying its heuristic function. Specifically, the Hybrid A* algorithm combines two heuristic functions: one based on non-holonomic constraints and the other based on dynamic programming. Experimental tests are conducted under various map expansions and branching parameters to compare the performance of these two algorithms in terms of path length, execution time, and path smoothness at corners. The results demonstrate that, with smaller branching parameters, the Hybrid A* algorithm generates shorter paths. However, in highly complex mazes, the path length of the Hybrid A* algorithm may be longer, but it exhibits smoother movements at corners.

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“…Many strategies for addressing trajectory planning have been studied over time by researchers: graph search [3,4], random sampling [5,6], artificial potential field (APF) [7][8][9], numerical optimization [10,11], and AI-based techniques [12]. For example, the twoway A-star method is used in [3] to increase the planning algorithm's computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the twoway A-star method is used in [3] to increase the planning algorithm's computational efficiency. In order to solve the problem that the traditional A-star algorithm has many inflection points and a long path, the study [4] proposed an improved A-star algorithm that increases the number of search directions. In [6], a novel approach using the Dijkstra optimization-based rapidly exploring random tree algorithm (APSD-RRT) is suggested; it has customizable probability and sample area, and experiments are carried out to show that the proposed approach can achieve significantly better performance in terms of balancing the computation cost and performance compared to original RRT.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Trajectory Planning Approach for Autonomous Ground Vehicles Using Improved Artificial Potential Field

Jin,

Li,

Opinat Ikiela

et al. 2024

Symmetry

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In this paper, the concept of symmetry is utilized in the promising trajectory planning design of autonomous ground vehicles—that is, the construction and the solution of improved artificial potential field-based trajectory planning approach are symmetrical. Despite existing artificial potential fields (APF) achievements on trajectory planning in autonomous ground vehicles (AGV), applying the traditional approach to dynamic traffic scenarios is inappropriate without considering vehicle dynamics environment and road regulations. This paper introduces a highly efficient approach for planning trajectories using improved artificial potential fields (IAPF) to handle dynamic road participants and address the issue of local minima in artificial potential fields. To begin with, potential fields are created with data obtained from other sensors. By incorporating rotational factors, the potential field will spin when the obstacle executes a maneuver; then, a safety distance model is also developed to limit the range of influence in order to minimize the computational burden. Furthermore, during the planning process, virtual forces using the gradient descent method are generated to direct the vehicle’s movement. During each timestep, the vehicle will assess whether it is likely to encounter a local minimum in the future. Once a local minimum is discovered, the method will create multiple temporary objectives to guide the vehicle toward the global minimum. Consequently, a trajectory that is both collision-free and feasible is planned. Traffic scenarios are carried out to validate the effectiveness of the proposed approach. The simulation results demonstrate that the improved artificial potential field approach is capable of generating a secure trajectory with both comfort and stability.

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A-star algorithm for expanding the number of search directions in path planning

Cited by 15 publications

References 5 publications

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Modified A* algorithm for path smoothing and obstacle avoidance

An Efficient Trajectory Planning Approach for Autonomous Ground Vehicles Using Improved Artificial Potential Field

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