用AI改进机器人路径规划，让轨迹更短更顺。
Performance Comparison of Classical and Neural Sampling Algorithms for Robotic Navigation
- 用神经网络引导采样，优化路径生成。
- 路径最短减少14%，轨迹平滑度提升55%~75%。
- 适合追求高效导航的机器人与无人机应用。
将人工智能融入基于采样的运动规划，为提升自主导航效率带来新可能。本文在包含凸凹障碍物且障碍物密度不同的环境中，实现了并评估了RRT*、Neural RRT*和Neural Informed RRT*三种算法。结果表明，神经网络引导的规划器显著提升路径质量，相较传统RRT*算法，路径长度最短缩短14%，轨迹平滑度提高55%至75%。在所评估方法中，Neural Informed RRT*在路径长度与轨迹平滑性方面表现最优。这些结果验证了AI引导采样策略在提升机器人与无人机导航可靠性与轨迹效率方面的有效性，尽管计算时间略有增加。研究凸显了人工智能在实时机器人路径规划应用中的日益重要性。
原文摘要 · Abstract (English)
Integrating artificial intelligence (AI) into sampling-based motion planning provides new possibilities for improving autonomous navigation efficiency. In this paper, three algorithms, namely RRT*, Neural RRT*, and Neural Informed RRT*, are implemented and evaluated on environments containing convex and concave obstacles with different obstacle densities. The obtained results indicate that neural-guided planners improve path quality, producing up to 14\% shorter paths and 55--75\% smoother trajectories compared with the conventional RRT* algorithm. Among the evaluated methods, Neural Informed RRT* achieves the best overall performance in terms of path length and trajectory smoothness. These results demonstrate the effectiveness of AI-guided sampling strategies for improving reliability and trajectory efficiency in robotic and UAV navigation, despite a slight increase in computation time. Overall, the study highlights the growing importance of artificial intelligence in real-time robotic path planning applications.
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