基于威胁度评估的机器人神经动态避撞策略

doi:10.13973/j.cnki.robot.2017.0853

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引用本文:
倪天, 魏瑞轩, 赵晓林, 许卓凡. 基于威胁度评估的机器人神经动态避撞策略[J]. 机器人, 2017, 39(6): 853-859.DOI: 10.13973/j.cnki.robot.2017.0853. NI Tian, WEI Ruixuan, ZHAO Xiaolin, XU Zhuofan. Neural Dynamic Collision-Avoidance Strategy for Robots Based on Evaluation of Threat Degree. ROBOT, 2017, 39(6): 853-859. DOI: 10.13973/j.cnki.robot.2017.0853.

摘要针对动态环境下移动机器人的自主碰撞规避，构建了包含感知模块、威胁规避模块和目标牵引模块的神经避撞决策系统.通过深入分析采样周期内威胁与机器人的相对速度、激光雷达有效探测距离以及威胁预测方位等因素对避撞机动效率和安全性的影响，对机器人各机动速度区的危险等级进行划分并建立相应的威胁度定量评估模型，再将其引入局部决策器以修正各节点的输出，进而提出了一种基于机动策略威胁度评估的神经动态避撞方法.仿真实验表明，相比于传统的神经导航，所提方法不仅优化了规避路径，而且代价更小.

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关键词 ：移动机器人, 碰撞规避, 威胁度评估, 神经导航, 路径代价

Abstract：For the autonomous collision-avoidance of mobile robots in dynamic environments, a neural collision-avoidance decision system consisting of a sensory module, a threat-avoidance module and a target-directed module is constructed. The relative velocity between threats and robot, effectual detection range of lidar, and prediction azimuth of threat within the sampling period are investigated, and their effects on the maneuvering efficiency and security of collision avoidance are analyzed deeply. Based on this, the risk level of each maneuvering area is determined, and a quantitative evaluation model of threat degree is established. Then, it is introduced into the local decision maker to correct the output of each node. Furthermore, a neural dynamic collision-avoidance method based on evaluation of threat degree of the maneuvering strategy is proposed. Simulation experiment results indicate that, compared with traditional neural network based navigation approaches, the proposed approach can optimize avoidance paths with a lower cost.

Key words： mobile robot collision avoidance evaluation of threat degree neural navigation path cost

收稿日期: 2017-01-05 录用日期: 2017-05-24

TP242

基金资助:国家自然科学基金（61503405，61573373）

通讯作者: 魏瑞轩,rxwei369@sohu.com E-mail: rxwei369@sohu.com

作者简介: 倪天(1993-),男,硕士生.研究领域:无人系统智能化决策
魏瑞轩(1968-),男,博士,教授.研究领域:无人系统自主控制与应用
赵晓林(1982-),男,博士,副教授.研究领域:机器人视觉导航.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0853 或 https://robot.sia.cn/CN/Y2017/V39/I6/853

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