足式机器人触地检测方法的研究综述

doi:10.3785/j.issn.1008-973X.2024.02.012

浙江大学学报(工学版)

2024, Vol. 58

Issue (2): 334-348 DOI: 10.3785/j.issn.1008-973X.2024.02.012

机械工程

足式机器人触地检测方法的研究综述

姜晓勇1,2(

),应凯健1,吴起威1,魏璇1

1. 浙江科技学院机械与能源工程学院，浙江杭州 310000
2. 浙江大学机械工程学院，浙江杭州 310058

Research overview on touchdown detection methods for footed robots

Xiaoyong JIANG1,2(

),Kaijian YING1,Qiwei WU1,Xuan WEI1

1. School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310000, China
2. School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China

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摘要：

基于现有足式机器人触地检测方法的研究，综合论述了腿部结构设计、足端设计、传感器设计对触地检测的影响. 总结外部传感器直接检测的触地检测方法、基于运动学与动力学的触地检测方法以及基于学习的触地检测方法. 归纳地面湿滑、地面松软以及非足端触地这3种特殊场景中的触地检测方法. 分析触地检测技术的应用场景，具体包括运动控制的需要、导航中的应用、地形与地质的感知这三大应用场景. 指出硬件改进和集成、多模态触地检测、多传感器融合化触地检测以及智能化触地检测这四大触地检测方法相关的发展趋势，总结各触地检测算法之间的具体关联，为触地检测后续技术的发展及触地检测的具体应用提供指导.

关键词： 足式机器人; 触地检测; 力传感器; 状态估计; 足端力估计

Abstract:

The effects of leg structure design, foot-end design and sensor design on touchdown detection were comprehensively discussed by analyzing the existing legged robot touchdown detection methods. The touchdown method for direct detection of external sensors, the touchdown detection method based on kinematics and dynamics, and the touchdown detection method based on learning were summarized. Touchdown detection methods were summarized in three special scenarios: slippery ground, soft ground, and non-foot-end contact. The application scenarios of touchdown detection technology were analyzed, including the three application scenarios of motion control requirements, navigation applications, and terrain and geological sensing. The development trends were pointed out, which related to the four major touchdown detection methods of hardware improvement and integration, multi-mode touchdown detection, multi-sensor fusion touchdown detection, and intelligent touchdown detection. The specific relationships between various touchdown detection algorithms were summarized, which provided guidance for the development of follow-up technology for touchdown detection and specific applications of touchdown detection.

Key words: legged robot touchdown detection force sensor state estimation foot-end force estimation

收稿日期: 2023-07-06 出版日期: 2024-01-23

CLC:

TH 113

基金资助: 国家自然科学基金资助项目（51675480）

作者简介: 姜晓勇（1976—），男，教授，从事机器人、智能制造、运动控制、机器视觉的研究. orcid.org/0000-0002-4791-7954. E-mail：119082@zust.edu.cn

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引用本文:

姜晓勇,应凯健,吴起威,魏璇. 足式机器人触地检测方法的研究综述[J]. 浙江大学学报(工学版), 2024, 58(2): 334-348.

Xiaoyong JIANG,Kaijian YING,Qiwei WU,Xuan WEI. Research overview on touchdown detection methods for footed robots. Journal of ZheJiang University (Engineering Science), 2024, 58(2): 334-348.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.02.012 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I2/334

图 1 典型足式机器人的实物图

表 1 机器人足端形状的对比分析

表 2 机器人足端材料的对比分析

表 3 机器人足端传感器的对比分析

表 4 各足式机器人的腿部设计

表 5 基于力传感器的触地检测方法对比

表 6 不同卡尔曼滤波器的特点

图 2 基于卡尔曼滤波器的触地检测原理图

算法框架	算法	所需状态量	算法原理	创新与改进
基于卡尔曼滤波器	EKF^[51]	全局坐标系下的机身质心位置、速度以及在世界坐标系下的4条腿足端位置	通过卡尔曼滤波器，融合运动编码器数据与机载IMU测量数据，估计机器人的状态及触地情况.	融合运动编码器数据与机载IMU测量数据，提高了触地状态估计的准确性.
	UKF^[45]			在更新步骤中引入异常值抑制方法，提高了算法的鲁棒性.
	IEKF^[52]			利用李群理论和不变观测器设计，开发接触辅助不变扩展卡尔曼滤波器，相对QEKF表现出优越的收敛性和一致性.
	KF^[53]	全局坐标系下的机身质心位置、速度以及机器人广义坐标位移、广义坐标速度	基于离散时间动量的观测器来估计腿上的外力，利用卡尔曼滤波器，将它与地面接触的高度概率模型和力概率模型融合，估计接触状态.	提出基于离散时间动量的观测器GM进行腿部外力估计的方法，将估计腿上的外力、测量概率模型进行卡尔曼融合，提高了接触状态估计的准确性.
基于粒子滤波器	PF^[55]	机器人广义坐标位移、广义坐标速度	使用速度约束来生成1组一维的可能接触点，通过粒子滤波器过滤掉不符合的接触点.	提出使用速度测量进行本体感觉接触定位的运动学方法，利用粒子滤波器过滤掉不符合的接触点，减少接触位置的不确定性.
隐马尔可夫模型	HMM^[56]	机器人广义坐标位移、广义坐标速度、IMU测量信息	融合动力学和正向/微分运动学信息以及IMU信息，构成完整的概率模型，进行接触状态估计.	提出概率接触检测策略，该策略考虑全动力学和微分/正运动学，以最大限度地利用可用信息进行接触估计，提升了鲁棒性及响应速度.
监督学习	SL^[57]	机器人广义坐标位移、广义坐标速度及扭矩、IMU测量信息	利用动力学、关节位置和扭矩测量中估计的GRF，对不同类型步态的不同GRF阈值进行编码处理，开展回归分析，得到机器人的接触概率.	该方法利用逻辑分类器学习概率最高的GRF阈值，使基本速度误差最小.
无监督学习	DL^[61]	机器人广义坐标位移、广义坐标速度、IMU测量信息以及力传感器的测量信息	提出基于本体感觉的深度学习框架，特别是在每条腿上安装有力/力矩传感器和IMU，确定接触状态概率，即动态步行行走在可变摩擦表面上的稳定或滑动/无接触概率.	仅采用本体感觉传感，尽管它依赖于模拟的地面真实接触数据进行分类过程，但算法可以推广到不同的摩擦表面和不同的腿式机器人平台，很容易从模拟转移到实践.
强化学习	RL^[62]	机器人广义坐标位移、广义坐标速度	通过强化学习训练运动策略，推断地形属性，如高度图、摩擦、障碍物.	提出新的运动学习框架，该框架通过不对称Actor-Critic架构，仅使用本体感觉就能推断地形属性.

表 7 触地状态估计算法的对比

图 3 特殊场景下的触地检测

图 4 基于触地检测的地形与地质感知

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