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In order to solve the problem of path redundancy and long algorithm execution time, this paper proposed a path planning method that combines the Northern Goshawk Optimization (NGO) algorithm with the improved rapidly-exploring random tree (RRT*). First, a fitness function with obstacle avoidance and goal orientation was designed to optimize the initial NGO population. Additionally, the adaptive sampling step size of the RRT* algorithm was designed according to the fitness function to improve the search efficiency in a large-scale map. Then, the optimal neighbor node sampling mechanism was designed to simulate behavior of the northern goshawk transmitting information to its nearest companions, while also the RRT* node sampling was constrained by considering the USV’s (unmanned surface vehicle) heading angle. Finally, in order to improve path smoothness, the Metropolis criterion was introduced and the smoothness and minimum rudder angle design fitness function were combined to select a more suitable parent node for dynamic rerouting. The experimental results show that compared with RRT*, Informed-RRT* and RRT*-smart algorithms, the improved algorithm reduces the path length by 19.36%, 3.36% and 5.98%, and decreases the search time by 49.33%, 57.01% and 59.16%, respectively. At the same time, the curvature of the path also decreases significantly.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 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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