Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network
Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network
复制标题
使用 3D 卷积神经网络预测杂乱环境中操纵规划的瓶颈点
DOI：
10.1109/iccma46720.2019.8988766
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
V. Kalaichelvi
中科院分区：
文献类型：
--
作者：
Indraneel Patil;B. Rout;V. Kalaichelvi
工业机器人的最新研究旨在使人类机器人的无缝协作成为可能。为此，工业机器人需要在非结构化和杂乱的环境中动态工作，因此感知驱动的运动规划起着至关重要的作用。事实证明，基于采样的运动规划器对于此类具有实时约束的高维规划问题是最有效的。不幸的是，随机采样器会遇到“狭窄通道”或瓶颈区域的现象，需要有针对性的采样来提高收敛速度。在一系列不同的规划问题中识别这些瓶颈区域也是一个挑战。本文尝试通过为快速探索随机树星 (RRT*) 规划器设计智能“瓶颈引导”启发式来解决这两个问题，该规划器基于使用 3D 卷积神经网络从规划场景中提取的相关上下文，并且还证明了所提出的技术可以推广到未见过的问题实例。本文将该技术（瓶颈引导 RRT*）与 10% 目标偏向 RRT* 规划器进行了基准测试，显示了规划时间和内存需求的显着改进，并使用 ABB 1410 工业机械手作为结果植入和验证的平台。
Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling based motion planners are proven to be the most effective for such high dimensional planning problems with real time constraints. Unluckily random stochastic samplers suffer from the phenomenon of ‘narrow passages’ or bottleneck regions which need targeted sampling to improve their convergence rate. Also identifying these bottleneck regions in a diverse set of planning problems is a challenge. In this paper an attempt has been made to address these two problems by designing an intelligent ‘bottleneck guided’ heuristic for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on relevant context extracted from the planning scenario using a 3D Convolutional Neural Network and it is also proven that the proposed technique generalizes to unseen problem instances. This paper benchmarks the technique (bottleneck guided RRT*) against a 10% Goal biased RRT* planner, shows significant improvement in planning time and memory requirement and uses ABB 1410 industrial manipulator as a platform for implantation and validation of the results.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram