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To address the rapid particle convergence and the decrease of particle diversity during map construction, as well as the tendency of the traditional DWA to become trapped in local optima during the path planning, the paper proposes two improvements for intelligent vehicles. The first improvement is an enhanced Gmapping algorithm based on K-Means hierarchical re-sampling. The particle set is clustered into high-, medium- and low- weight groups by using K-Means algorithm, and the weights are adjusted to slow down the decline in particle diversity, thereby improving mapping accuracy. The second improvement is an enhanced DWA path planning algorithm that fuses A* global guidance with turn-stability awareness. The adaptive velocity evaluation function considering the angular velocity magnitude, and a separate angular velocity evaluation function are added. The A* global path turning points serve as the key points to integrate the A* and DWA algorithms. Together, these two efforts improve the global optimization ability of the DWA algorithm. The simulation and real vehicle testing results show that the improved Gmapping algorithm increases the average number of effective particles by 4.6% during grid-map construction. The improved DWA algorithm reduces the number of global path turns by 67% and the search nodes by 37.5% under the set scenario, effectively improving the turning stability of intelligent vehicles.
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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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