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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (2): 334-348    DOI: 10.3785/j.issn.1008-973X.2024.02.012
    
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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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     
Received: 06 July 2023      Published: 23 January 2024
CLC:  TH 113  
Fund:  国家自然科学基金资助项目（51675480）
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Xiaoyong JIANG
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Xuan WEI
Cite this article:

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.

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


足式机器人触地检测方法的研究综述

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 
Fig.1 Physical image of typical footed robots
足端形状优缺点分析典型机器人
平面型 优点：接触面积较大，能够提供较大的附着力，承重能力较强 SILO4[8]、Biosbot[9]、TITAN[10]、
PETMAN[11]、HRP-3[12]、ATLAS[13]
缺点：适应性不高，在复杂地形下，容易造成机器人行走不稳
半圆型 优点：点接触足端，地形适应性较强，在复杂地形环境中应用广泛 ANYmal[6]、MiniCheetah[14]、HYQ[15]、
UnitreeA1[16]、CyberDog[17]、LittleDog[18]
缺点：在山区类地形中，足端与地面接触面小，导致附着力性能不好，容易打滑
圆柱型 优点：线接触足端，复杂地形适应性好，结构简单 BigDog[4]、 Cheetah 3[5]
Jueying[19]、Pegasus[20]
缺点：线接触，容易导致接触面积不够，大斜度地面容易打滑
仿生型 优点：进行仿生设计，通过模仿各类行走时附着性能高的动物足端，提高对复杂环境的适应性 SandBot[21]、Stickybot[22]
RHex[23]、Azimuth[24]
Cockroach[25]
缺点：仿生型足端，结构较复杂，耐久性仍需验证
Tab.1 Comparative analysis of robot foot end shapes
足端材料接触性可变性耐磨性强度质量典型机器人
橡胶较好好较好较低较小ANYmal[6]、MiniCheetah[14]
聚合物较差较差较好较低小Hexapod[26]、Laikago[27]
金属差差好高大PETMAN[11]、ATLAS[13]
3D打印（非橡胶类）较差较差较低较低小Ji[28]、Muralidharan[29]
Tab.2 Comparative analysis of robot foot end materials
传感器类型优缺点分析参考文献
力/扭矩传感器 优点：高精度，宽量程，高灵敏度，耐用. 文献[30]、[31]
缺点：质量偏大，位于足端会增加惯性，高速、高冲击下会出现惯性噪声.
压敏电阻传感器 优点：结构简单，质量小，耐用度较高. 文献[32]、[33]
缺点：有限量程、受温度影响，不适合高精度、高动态测量.
电容触觉传感器 优点：测量范围大，温度稳定性好，结构简单，适应性强. 文献[34]
缺点：输出阻抗高，负载能力差，寄生电容影响大.
气压传感器 优点：结构轻巧，有良好的顺应性和阻尼性. 文献[35]、[36]
缺点：动态响应较慢，寿命不长.
霍尔效应传感器 优点：精度高、响应速度快、无接触测量、具有线性特性、抗干扰能力强. 文献[37]
缺点：灵敏度不高、温度漂移明显、对磁场幅值和方向非常敏感、成本较高.
光学触觉传感器 优点：高过载范围、高精度、抗电磁干扰、非接触性、无损测量. 文献[38]
缺点：受光照影响，光源不足，处理速度不够快.
自驱力传感器 优点：独立性、省电、灵活性和响应速度快. 文献[42]
缺点：能源和激励信号的限制、设计和实现的复杂性、受环境噪声干扰的影响.
Tab.3 Comparative analysis of robot foot end sensors
机器人机器人
质量		腿部结构足端形状足端
材料		触地检测传感器已使用的触
地检测方法		应用场景
BigDog[4]重型串联式+弹簧圆柱型橡胶压力传感器基于力传感器的触地检测军事
Cheetah 3[5]中型串联式圆柱型橡胶无足端力估计+触地状态机巡检、救援
ANYmalC[6]中型串联式半圆型橡胶无多传感器融合+足端力估计、深度强化学习（间接保证）工业巡检
Minitaur[7]轻型并联式半圆型橡胶无强化学习（间接保证）、基于速度的接触定位救援、勘探
ATLAS[13]重型串联式平面型金属力/扭矩传感器多传感器融合+足端力估计、深度强化学习（间接保证）工业生产、巡检、救援
Jueying[19]中型串联式圆柱型橡胶无足端力估计、深度强化学习（间接保证）巡检、救援、排爆
RHex[23]轻型轮腿式仿生型橡胶无触地状态机勘探、救援
Tab.4 Leg design of each legged robot
文献力传感器种类有无编码
器融合		有无IMU
融合		检测原理与应用
文献[43]接触开关无无利用接触开关的开关信息来判断触地信息
文献[44]二进制接触传感器无无利用二进制接触传感器的接触阈值来构成有限状态机，实现接触状态的切换
文献[45]二进制接触传感器有有利用二进制接触传感器来确定腿部接触，用支撑腿的运动学约束来更新无迹卡尔曼滤波器，提高滤波器的鲁棒性
文献[46]3轴力-扭矩传感器有有利用力传感器GRF值对接触地状态进行检测，选用触地状态下的静止腿进行状态估计
文献[47]力-扭矩传感器有有使用模糊c-均值算法，对人形机器人脚部的F/T和IMU测量值进行聚类，独立估计每条腿的接触概率
文献[48]脚敏电阻器（FSR）有有利用力传感器压力对接触地状态进行检测，融合编码器、惯性、视觉里程测量，开展机器人的综合状态估计
文献[49]力-扭矩传感器有有根据拉格朗日动力学方程得到的关节力矩和力传感器得到的关节力建立触地检测函数，确定四足机器人脚的触地状态，实现腿的相位切换
Tab.5 Comparison of force sensor-based touchdown detection method
滤波器种类适用范围滤波器特点
KF线性系统对非线性系统的估计效果差
EKF非线性系统泰勒近似存在正反馈和一致性问题
UKF 非线性系统Sigma点集过大
IEKF 非线性系统特定系统中误差自治，与轨迹无关
Tab.6 Characteristics of different Kalman filters
Fig.2 Schematic diagram of touchdown detection based on Kalman filter
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.
Tab.7 Comparison of touchdown state estimation algorithm
Fig.3 Touchdown detection in special scenarios
Fig.4 Terrain and geological perception based on touchdown detection
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