动态调整采样参数，让机器人规划更智能高效
Deep Fuzzy Optimization for Batch-Size and Nearest Neighbors in Optimal Robot Motion Planning
- 基于深度模糊学习，自适应调节批量大小和邻近点数
- 在高维空间中收敛更快，路径成本更低，计算时间减少
- 适合复杂障碍环境下的实时机器人运动规划
高效的运动规划算法对机器人至关重要。优化采样方法中的关键参数，如批量大小和最近邻选择，可提升规划性能。然而，现有方法往往缺乏环境适应性。受深度模糊神经网络启发，本文提出学习型知情树（LIT*），一种基于采样的深度模糊学习规划器，能根据配置空间中的障碍分布动态调整批量大小与最近邻参数。通过编码有效与无效状态的全局与局部比例，LIT* 可区分障碍稀疏与密集区域，从而生成更低代价路径并减少计算时间。实验结果表明，在 R^8 至 R^14 的高维空间中，LIT* 收敛速度更快，解的质量更高，优于当前最先进的单查询采样式规划器，并成功应用于双臂机器人操作任务。视频演示见：https://youtu.be/NrNs9zebWWk
原文摘要 · Abstract (English)
Efficient motion planning algorithms are essential in robotics. Optimizing essential parameters, such as batch size and nearest neighbor selection in sampling-based methods, can enhance performance in the planning process. However, existing approaches often lack environmental adaptability. Inspired by the method of the deep fuzzy neural networks, this work introduces Learning-based Informed Trees (LIT*), a sampling-based deep fuzzy learning-based planner that dynamically adjusts batch size and nearest neighbor parameters to obstacle distributions in the configuration spaces. By encoding both global and local ratios via valid and invalid states, LIT* differentiates between obstacle-sparse and obstacle-dense regions, leading to lower-cost paths and reduced computation time. Experimental results in high-dimensional spaces demonstrate that LIT* achieves faster convergence and improved solution quality. It outperforms state-of-the-art single-query, sampling-based planners in environments ranging from R^8 to R^14 and is successfully validated on a dual-arm robot manipulation task. A video showcasing our experimental results is available at: https://youtu.be/NrNs9zebWWk
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