3D path planning is a key technology in fields such as UAV navigation and intelligent inspection, and its planning performance directly impacts mission effectiveness. Traditional ant colony algorithms adopt fixed parameters, which frequently give rise to problems including slow convergence speed, a tendency to fall into local optimal solutions, and in sufficient global search capability in complex 3D environments. To overcome these limitations, this paper presents a triple-adaptive improved ant colony optimization algorithm. By adaptively adjusting the pheromone factor α and the heuristic factor β via a logarithmic function, and by designing a constrained adaptive pheromone evaporation coefficient ρ , the algorithm enhances both global search and local optimization capabilities. Experimental results demonstrate that, compared to the traditional algorithm, the improved algorithm generates smoother paths, with the optimal fitness improved by 15.54% and the algorithm runtime efficiency improved by 24.15%, demonstrating better comprehensive performance in both path quality and computational speed. This effectively overcomes the original deficiencies and meets the requirements of path planning in complex 3D environments.
Yunxiu Wang, Wei Gao, Ruopu Bai et al.· International Conference on...· 0 citations
In recent years, autonomous driving technology has entered a new phase of rapid development. Accurate positioning information, as the core premise for path planning, obstacle avoidance decision-making and stable control of autonomous vehicles, directly determines the safety of system operation. LiDAR has emerged as a crucial positioning sensor in autonomous driving perception systems due to its high-precision ability for 3D environment modeling. However, under complex meteorological conditions such as snowfall, rainfall and fog, the laser radar point cloud is prone to being disturbed by noise, which will lead to a decrease in positioning accuracy and thus become a key bottleneck that severely restricts the reliable deployment of autonomous driving systems in all weather conditions. To address these issues, this paper proposes a point cloud denoising and restoration method based on multi-feature fusion. This method utilizes the temporal information of multiple frames of point clouds, distinguishes dynamic objects from noise through motion trajectory analysis, restores the environment feature points that were wrongly deleted, and achieves precise denoising through multi-feature weighted fusion. To verify its effectiveness, this paper conducted data collection and experimental analysis under various harsh weather conditions such as snowfall, rainfall and simulated fog in both hardware-in-the-loop simulation and real scenarios. The results showed that compared with traditional methods, the proposed method's comprehensive denoising metric F-score increased by more than 15%, significantly improving the point cloud quality under adverse weather conditions, and providing effective technical support for the stable positioning of autonomous driving in complicated meteorological environments.
Ying Wang, Jian Zhang, Xiong Yan et al.· International Conference on...· 0 citations
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