Guided Hybrid A-star Path Planning Algorithm for Valet Parking Applications

Sedighi, Saeid; Nguyen, Duong-Van; Kuhnert, Klaus-Dieter

doi:10.1109/iccar.2019.8813752

Cited by 100 publications

(31 citation statements)

References 6 publications

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“…Improved hierarchical A* is claimed to have improved efficiency and precision [36]. Another variation of this algorithm is used in valet parking where the ego-vehicle (cars that usually focus only on their local environment and do not take into account environmental context) has a direction of motion d that depends on the speed, gear, steering angle, and other parameters of the vehicle kinematics [34,37].…”

Section: A* Algorithmmentioning

confidence: 99%

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A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

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Section: A* Algorithmmentioning

confidence: 99%

“…The start, end, and obstacle positions are fed into as input to the Hybrid A* algorithm where A* is run on the results of the visibility diagram, which then provides the optimum distance. This is called the Guided Hybrid A* algorithm [37]. Common heuristic functions appear in Table 2.…”

Section: A* Algorithmmentioning

confidence: 99%

A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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“…The A-Star algorithm guides the optimal path to the goal if the heuristic function h(n) is acceptable, meaning that it will never overestimates the original cost [19] or actual cost [20]. Evaluation function f(n) = g(n) + h(n), where [17][21] [22]: g(n) = cost so far to reach n. h(n) = estimated cost from n to target (goal). f(n) = estimated total cost of the path through n to the target.…”

Section: A-star Algorithmmentioning

confidence: 99%

Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

Wayahdi

Ginting

Syahputra

2021

Int. J. Adv. Data Inf. Syst.

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The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

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“…Therefore, the efficiency of the A* algorithm is better than Dijkstra's. Many works of literature show that the A* algorithm has been widely used in many fields, such as transportation industry [14,15], and with the development of artificial intelligence, the A* algorithm has since been improved, including automated guided vehicle (AGV) [16,17] or unmanned surface vehicle (USV) path planning [18,19] and robot path planning [20,21]. The A* algorithm is simpler and uses less search node, which means less computation than some other path planning algorithms, so it lends itself to more constrained scenarios [22,23].…”

Section: Related Workmentioning

confidence: 99%

“…A visibility diagram was one of the very first graph-based search algorithms used in path planning in robotics [10,31]. A hybrid A* algorithm with the visibility diagram planning algorithm is proposed in [14] to overcome the issue of high run costs to convergence.…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Robust Improved A* Algorithm for Path Planning

Wang¹,

Qi²,

Lou³

et al. 2021

Symmetry

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Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

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Guided Hybrid A-star Path Planning Algorithm for Valet Parking Applications

Cited by 100 publications

References 6 publications

A Survey of Path Planning Algorithms for Mobile Robots

A Survey of Path Planning Algorithms for Mobile Robots

Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

An Efficient and Robust Improved A* Algorithm for Path Planning

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