Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A...
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MDPI AG
2025-05-01
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| author | Hang Zhou Tianning Shang Yongchuan Wang Long Zuo |
| author_facet | Hang Zhou Tianning Shang Yongchuan Wang Long Zuo |
| author_sort | Hang Zhou |
| collection | DOAJ |
| description | The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2–0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios. |
| format | Article |
| id | doaj-art-75989893c43247f080a3b61575eaed56 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-75989893c43247f080a3b61575eaed562025-08-20T03:14:29ZengMDPI AGApplied Sciences2076-34172025-05-011510558310.3390/app15105583Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path PlanningHang Zhou0Tianning Shang1Yongchuan Wang2Long Zuo3School of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaThe efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2–0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios.https://www.mdpi.com/2076-3417/15/10/5583salp swarm algorithmA* algorithmpath smoothingB-spline interpolation |
| spellingShingle | Hang Zhou Tianning Shang Yongchuan Wang Long Zuo Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning Applied Sciences salp swarm algorithm A* algorithm path smoothing B-spline interpolation |
| title | Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning |
| title_full | Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning |
| title_fullStr | Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning |
| title_full_unstemmed | Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning |
| title_short | Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning |
| title_sort | salp swarm algorithm optimized a algorithm and improved b spline interpolation in path planning |
| topic | salp swarm algorithm A* algorithm path smoothing B-spline interpolation |
| url | https://www.mdpi.com/2076-3417/15/10/5583 |
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