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Path Planning for Deep-Sea Collector Vehicles Based on Integration of RRT Algorithm and Artificial Potential Field Algorithm
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Qiaoyu CUI, Saifeng PENG, Hao ZHENG, Rui WANG
Mining and Metallurgical Engineering | 2026, 46(1) : 7 - 16
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Mining and Metallurgical Engineering | 2026, 46(1): 7-16
• MINING •
Path Planning for Deep-Sea Collector Vehicles Based on Integration of RRT Algorithm and Artificial Potential Field Algorithm
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Qiaoyu CUI, Saifeng PENG, Hao ZHENG, Rui WANG
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  • State Key Laboratory of Exploitation and Utilization of Deep-Sea Mineral Resources, Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China
doi: 10.3969/j.issn.0253-6099.2026.01.002
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An improved algorithm combining Rapidly-Exploring Random Trees (RRT) and Artificial Potential Field (APF) was proposed for obtaining an overall path planning for deep-sea collector vehicles. Based on the kinematic model of the collector vehicle, the RRT sampling mechanism was optimized by a target-biased strategy, and an adaptive repulsive coefficient was introduced to improve the potential field function, thereby enhancing obstacle avoidance and target reachability. Verification was carried out by simulation under two different working conditions. The results show that in Condition 1 (complex environment), the RRT*-APF algorithm has its searching time shortened by 73.1%, the number of expanded nodes reduced by 50.7%, and the path length shortened from 1 542.35 m to 1 486.16 m compared to the RRT* algorithm, being more suitable for complex environments. In Condition 2 (simple environment), the RRT-APF algorithm has its searching time shortened by 67.4%, the number of expanded nodes reduced by 52.6%, and the path length shortened from 973.58 m to 910.27 m compared to the traditional RRT algorithm, being more suitable for simple environments.

deep-sea mining  /  collector vehicle  /  path planning  /  Rapidly-Exploring Random Trees (RRT)  /  Artificial Potential Field (APF)  /  repulsive coefficient  /  obstacle avoidance
崔乔宇, 彭赛锋, 郑皓, 王蕊. 基于融合RRT算法和人工势场法的深海采矿车路径规划研究. 矿冶工程杂志, 2026 , 46 (1) : 7 -16 . DOI: 10.3969/j.issn.0253-6099.2026.01.002
Qiaoyu CUI, Saifeng PENG, Hao ZHENG, Rui WANG. Path Planning for Deep-Sea Collector Vehicles Based on Integration of RRT Algorithm and Artificial Potential Field Algorithm[J]. Mining and Metallurgical Engineering, 2026 , 46 (1) : 7 -16 . DOI: 10.3969/j.issn.0253-6099.2026.01.002
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Year 2026 volume 46 Issue 1
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doi: 10.3969/j.issn.0253-6099.2026.01.002
  • Receive Date：2025-10-21
  • Online Date：2026-10-08
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  • Received：2025-10-21
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    State Key Laboratory of Exploitation and Utilization of Deep-Sea Mineral Resources, Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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