Path planning of unmanned ships based on A* and dynamic window approach
The coastline raid task requires an unmanned surface ship to carry out precise, fixed-point raids in a complex coastal environment. The terrain of the coastal area is highly varied, with static obstacles such as reefs and shoals, as well as moving obstacles like floating objects at sea. Moreover, th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2025-06-01
|
| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202519/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849252034004385792 |
|---|---|
| author | FU Long SHEN Zhen TAO Hao HAN Yunjun DONG Xisong XIONG Gang |
| author_facet | FU Long SHEN Zhen TAO Hao HAN Yunjun DONG Xisong XIONG Gang |
| author_sort | FU Long |
| collection | DOAJ |
| description | The coastline raid task requires an unmanned surface ship to carry out precise, fixed-point raids in a complex coastal environment. The terrain of the coastal area is highly varied, with static obstacles such as reefs and shoals, as well as moving obstacles like floating objects at sea. Moreover, the task must be completed within a strict time frame. Therefore, real-time, safe, and accurate path planning for the unmanned surface ship is crucial. To address the challenge of balancing global optimization, efficiency, and safety in path planning, a method that integrates global search and local optimization was proposed, based on the A* algorithm and the dynamic window approach (DWA). The A* algorithm computed the global shortest path in a static environment, while the improved DWA optimized local path obstacle avoidance between path nodes. The A* algorithm was enhanced by incorporating a heuristic function with dynamic exponential decay weighting, which reduced the running time and exploration space for global path planning. Additionally, the DWA was improved by introducing an evaluation function based on the angle between the heading of dynamic obstacles and the predicted trajectory of the unmanned surface ship, as well as a distance evaluation function based on a logistic curve, which enhanced the real-time responsiveness of local obstacle avoidance. Simulation results demonstrate that the proposed hybrid algorithm can search for a near-optimal global solution in real-time, effectively handle both static and dynamic obstacles, and significantly improve the task completion efficiency and stability of the unmanned surface ship in complex coastal environments. |
| format | Article |
| id | doaj-art-dc4a4a57d4c4492aaf89cb62e179c7f3 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-dc4a4a57d4c4492aaf89cb62e179c7f32025-08-20T03:56:45ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522025-06-017184199117464688Path planning of unmanned ships based on A* and dynamic window approachFU LongSHEN ZhenTAO HaoHAN YunjunDONG XisongXIONG GangThe coastline raid task requires an unmanned surface ship to carry out precise, fixed-point raids in a complex coastal environment. The terrain of the coastal area is highly varied, with static obstacles such as reefs and shoals, as well as moving obstacles like floating objects at sea. Moreover, the task must be completed within a strict time frame. Therefore, real-time, safe, and accurate path planning for the unmanned surface ship is crucial. To address the challenge of balancing global optimization, efficiency, and safety in path planning, a method that integrates global search and local optimization was proposed, based on the A* algorithm and the dynamic window approach (DWA). The A* algorithm computed the global shortest path in a static environment, while the improved DWA optimized local path obstacle avoidance between path nodes. The A* algorithm was enhanced by incorporating a heuristic function with dynamic exponential decay weighting, which reduced the running time and exploration space for global path planning. Additionally, the DWA was improved by introducing an evaluation function based on the angle between the heading of dynamic obstacles and the predicted trajectory of the unmanned surface ship, as well as a distance evaluation function based on a logistic curve, which enhanced the real-time responsiveness of local obstacle avoidance. Simulation results demonstrate that the proposed hybrid algorithm can search for a near-optimal global solution in real-time, effectively handle both static and dynamic obstacles, and significantly improve the task completion efficiency and stability of the unmanned surface ship in complex coastal environments.http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202519/coastline raidA* algorithmdynamic window approachpath planning |
| spellingShingle | FU Long SHEN Zhen TAO Hao HAN Yunjun DONG Xisong XIONG Gang Path planning of unmanned ships based on A* and dynamic window approach 智能科学与技术学报 coastline raid A* algorithm dynamic window approach path planning |
| title | Path planning of unmanned ships based on A* and dynamic window approach |
| title_full | Path planning of unmanned ships based on A* and dynamic window approach |
| title_fullStr | Path planning of unmanned ships based on A* and dynamic window approach |
| title_full_unstemmed | Path planning of unmanned ships based on A* and dynamic window approach |
| title_short | Path planning of unmanned ships based on A* and dynamic window approach |
| title_sort | path planning of unmanned ships based on a and dynamic window approach |
| topic | coastline raid A* algorithm dynamic window approach path planning |
| url | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202519/ |
| work_keys_str_mv | AT fulong pathplanningofunmannedshipsbasedonaanddynamicwindowapproach AT shenzhen pathplanningofunmannedshipsbasedonaanddynamicwindowapproach AT taohao pathplanningofunmannedshipsbasedonaanddynamicwindowapproach AT hanyunjun pathplanningofunmannedshipsbasedonaanddynamicwindowapproach AT dongxisong pathplanningofunmannedshipsbasedonaanddynamicwindowapproach AT xionggang pathplanningofunmannedshipsbasedonaanddynamicwindowapproach |