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For the problem that the autonomous navigation exploration algorithm is easy to fall into the local area, this paper proposed an exploration algorithm combining sampling and deep reinforcement learning. First, the Long-Short-Term Memory (LSTM) network was used locally to obtain the historical pose information of the unmanned vehicle to avoid repeated exploration of the explored area; secondly, the optimal action of the deep reinforcement learning strategy was used to output using deep reinforcement learning and the reward function was designed to encourage the unmanned vehicle to fully explore the unknown area; Finally, the horizontal movement factor of the unmanned vehicle was considered to generate a global exploration path conforming to its current attitude by solving the Asymmetric Travel Salesman Problem (ATSP). In the 2 000 s mine tunnel simulation environment, compared with the Technologies for Autonomous Robot Exploration (TARE) algorithm, the proposed algorithm increased the exploration area by 346.3 m2 and reduced the total driving distance by 209.4 m; in the real scene test, the exploration algorithm completed the exploration of the underground garage with an area of 3 444.3 m2 and returned to the starting point in 1 014 s and built the environment map.
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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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