Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps
Abstract
:1. Introduction
- (1)
- We designed an ENMSA-A* algorithm that adaptively adjusts the search weight of the heuristic function based on the obstacle distribution of the scene map, which adopts the fitting priority search strategy and can update the movement direction in real time according to the search location, which is suitable for a variety of scene maps, and the comprehensive performance is better than the traditional A* algorithm.
- (2)
- We determined the distribution of obstacles in the scene map by segmentation and then fusion and designed a more robust heuristic function on this basis.
- (3)
- According to the relative position relationship between vectors, we designed a new dynamic programming matrix and assigned priority in the extended 16 neighborhoods, so as to enhance the guidance and search speed of the ENMSA-A* algorithm.
- (4)
- By judging the distance and connectivity between each four turning points, a better route can be selected to obtain a relatively smooth path, which makes the ENMSA-A* algorithm more suitable for the daily work of mobile robots.
2. Traditional A* Algorithm
3. ENMSA-A* Algorithm
3.1. Adaptive Weight Strategy
3.2. Extend the Neighborhood Priority Search Strategy
3.3. Redundant Node Deletion Strategy
3.4. ENMSA-A* Algorithmic Process
3.5. Performance Robustness Evaluation Index Design of Algorithms
4. Comparative Analysis of ENMSA-A* Algorithm
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Algorithm | Robustness | Real-Time Adaptability | Search Efficiency | Path Optimization | Path Smoothness | |
---|---|---|---|---|---|---|
Stability | Performance Robustness | |||||
Improved A* [24] | moderate | weak | weak | weak | strong | moderate |
Improved A* [25] | strong | weak | moderate | weak | strong | strong |
Improved A* [26] | strong | weak | weak | moderate | weak | moderate |
Improved A* [27] | weak | weak | weak | weak | weak | weak |
Improved A* [28] | moderate | moderate | moderate | weak | weak | weak |
Improved A* [29,30,31,32] | moderate | moderate | strong | weak | moderate | strong |
Algorithm | Number of Nodes | Path Nodes | Number of Turns | Turn Angles (°) | Path Time (s) | Path Length (m) |
---|---|---|---|---|---|---|
Traditional A* | 280 | 32 | 9 | 405 | 0.702 | 36.7990 |
Adaptive weight strategy | 87 | 34 | 12 | 540 | 0.241 | 37.9706 |
Maps | Algorithm | Number of Nodes | Path Nodes | Number of Turns | Turn Angles (°) | Path Time (s) | Path Length (m) |
---|---|---|---|---|---|---|---|
(a) | Traditional A* | 242 | 32 | 12 | 540 | 0.673 | 37.5563 |
Adaptive weight strategy | 93 | 34 | 10 | 450 | 0.255 | 37.5563 | |
(b) | Traditional A* | 230 | 33 | 12 | 540 | 0.614 | 36.9706 |
Adaptive weight strategy | 78 | 33 | 10 | 450 | 0.231 | 36.9706 | |
(c) | Traditional A* | 163 | 32 | 12 | 540 | 0.412 | 36.3848 |
Adaptive weight strategy | 60 | 32 | 10 | 450 | 0.189 | 36.3848 |
Number of Nodes | Path Nodes | Number of Turns | Turn Angles (°) | Path Time (s) | Path Length (m) | ||
---|---|---|---|---|---|---|---|
Traditional A* algorithm | 8 search neighborhood | 280 | 32 | 9 | 405 | 0.709 | 36.7990 |
16 search neighborhood | 35 | 17 | 15 | 321.0588 | 0.394 | 41.0197 | |
Extend Neighborhood Priority Search Strategy | N = 1 | 35 | 21 | 13 | 386.3406 | 0.355 | 41.4274 |
N = 4 | 35 | 22 | 15 | 480.3623 | 0.331 | 41.6055 | |
N = 8 | 35 | 21 | 15 | 321.0588 | 0.302 | 41.0197 | |
N = 12 | 35 | 21 | 15 | 321.0588 | 0.302 | 41.0197 | |
N = 16 | 35 | 21 | 15 | 321.0588 | 0.302 | 41.0197 | |
N = 20 | 35 | 21 | 15 | 321.0588 | 0.302 | 41.0197 |
Traditional A* | Improved A* [25] | Improved A* [26] | Improved A* [27] | ENMSA-A* | |
---|---|---|---|---|---|
Number of nodes | 1 | 19.67 | 0 | 83.54 | 84.95 |
Path nodes | 1 | 39.74 | 38 | 74.24 | 83.84 |
Number of turns | 1 | 5.56 | 15.28 | 34.72 | 65.28 |
Turns angles | 1 | 47.78 | 38.16 | 55.54 | 77.38 |
Path time | 1 | −200.79 | −1.1 | 85.85 | 58.47 |
Path length | 1 | 8.92 | 2.28 | −19.47 | 3.36 |
V | 0 | 0.2827 | 1.2026 | 4.4438 | 5.5555 |
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You, Z.; Shen, K.; Huang, T.; Liu, Y.; Zhang, X. Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps. Electronics 2023, 12, 1004. https://doi.org/10.3390/electronics12041004
You Z, Shen K, Huang T, Liu Y, Zhang X. Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps. Electronics. 2023; 12(4):1004. https://doi.org/10.3390/electronics12041004
Chicago/Turabian StyleYou, Zhiyu, Keyu Shen, Tao Huang, Yongxin Liu, and Xiaofeng Zhang. 2023. "Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps" Electronics 12, no. 4: 1004. https://doi.org/10.3390/electronics12041004