绳驱动柔性上肢外骨骼机器人设计与控制

为了提高上肢外骨骼机器人的拟人化程度及关节柔性,设计了一种由串联弹性驱动器和鲍登线驱动的4自由度柔性上肢外骨骼机器人.首先,设计一种六连杆双平行四边形机构,建立肩关节虚拟转动中心,满足人体肩部3自由度运动需求.然后,设计基于串联弹性驱动器和鲍登线的驱动模块,将驱动器和机器人关节分离,降低结构的复杂度,减轻关节质量,实现力矩/位置信息的反馈.最后,构建机器人运动学及动力学模型,设计关节阻抗控制器并对样机肘关节进行阻抗控制实验.由实验结果可知,刚度系数在0.5 N·m/(°)～1.5 N·m/(°)时,力矩跟踪均方根为0.33 N·m;阻尼系数在0.001 N·m·s/(°)～0.01 N·m·s/(°)时,力矩跟踪均方根为0.57 N·m.实验结果表明,调节阻抗控制器中的阻抗系数能够改变关节的刚度和阻尼特性,从而提高人机连接的柔顺性.因此该机器人可以满足康复训练需求.     

气动人工肌肉拮抗关节的力与刚度独立控制

针对柔性仿生关节难以实现力与刚度独立控制的问题,建立了一种新的气动人工肌肉等效弹簧模型及关节力和刚度模型,设计了一种双输入双输出滑模控制器,来实现对气动人工肌肉拮抗关节力与刚度的独立控制．最后,搭建了气动人工肌肉驱动的拮抗关节实验平台,在关节位置固定和开放两种状况下进行了实验研究,验证所提方法的有效性;在不同负载情况下进行了对比实验,验证所提方法的通用性．所提出的建模和控制方法综合考虑了仿生关节位置、力和刚度相对独立控制,在机器人与人或环境互相作用的场合有很好的应用前景．     

面向助力膝关节外骨骼的弹性驱动器研制及实验研究

为满足下肢助力外骨骼不同行走模式下有效驱动的需求,提出了一种弹性驱动器,通过电机带动丝杠螺母串联弹簧,结合相应的刹车片,实现弹性驱动器对不同行走模式下的助力膝关节外骨骼的驱动.对弹性驱动器进行工作模式分析及刹车装置的动力学研究.为优选出合适的刹车片材料及弹簧,进行了弹性驱动器的刹车力及弹跳冲击实验.在建立的Solid Works、ADAMS虚拟样机联合仿真平台上对弹性驱动器驱动的膝关节外骨骼进行运动仿真,考察弹簧刚度及等效质量对弹性驱动器工作性能的影响,为下肢助力机器人弹性驱动器的设计提供理论依据.     

柔性关节机器人基于柔性补偿的奇异摄动控制

传统的奇异摄动方法仅适用于关节具有弱柔性的机器人的控制．为了解决这一问题,设计了关节柔 性补偿器,大大提高了关节的等效刚度,从而消除了关节柔性对该方法的限制,使得奇异摄动方法能够应用于关 节具有一般柔性的机器人系统中．此外,对于慢子系统的控制,选择以投影算法作为参数估计规律的自适应控制 器,并证明了它的渐近稳定性．该控制策略没有关节柔性限制,不需要连杆加速度及其微分信号,便于工程应用． 最后以本实验室的柔性关节机器人为研究对象进行了实验研究,验证了所提控制策略的有效性和可行性．     

一种基于肌电信号的自适应人机交互控制方法

针对踝关节康复机器人运动过程中的人机交互性问题, 本文提出一种基于肌电信号的鲁棒自适应人机交 互控制方法. 针对患者难以保持某一动作、肌电信号微弱等特点, 提出一种新的关节角度估计方法. 该方法充分利 用了踝关节运动时胫骨前肌与腓肠肌的拮抗关系, 将踝关节的动作类型与单个肌肉群的收缩进行关联, 利用归一化 的特征值完成运动意图的辨识和运动角度的估计. 为了保证人机交互的安全性, 提出一种刚度、阻尼参数在线自适 应调节的阻抗控制算法. 基于交互力矩对机器人末端的运动角度与运动速度实时进行调节, 使其对外表现出等效 柔性. 实验研究表明所提出的人机交互控制方法是有效的, 并具有一定应用前景.     

一种基于串联弹性驱动器的柔顺机械臂设计

为了应对工作环境的动态变化以及人机交互的不确定性,设计了基于被动柔顺结构和主动柔顺控制的柔顺机械臂Soft Arm II.在关节电机和连杆之间加入串联弹性驱动器(SEA)传动模块,SEA传动模块由线弹簧周向均布构成;建立了3DOF柔顺机械臂的运动学/动力学模型以及系统刚度模型,基于系统刚度模型提出工作空间典型位姿下关节刚度加权平均的SEA弹簧刚度确定方法;柔顺机械臂采用位置PID(比例-微分-积分)控制,并通过监控末端接触力和关节力矩适时修改指令轨迹.在柔顺机械臂Soft Arm II上执行了自由空间中的圆形轨迹跟踪、人机直线对推和碰撞模拟实验,结果显示,Soft Arm II在自由空间中具有较好的轨迹跟踪性能,能够实现与操作者的柔顺交互以及对碰撞的安全避让.基于SEA的被动柔顺结构设计以及基于末端力和关节力矩监控的控制策略能够满足人机共存环境对机械臂柔顺性及安全性的要求.     

弹性基和弹性关节空间机器人的自适应鲁棒抗扰控制及振动抑制

研究不确定弹性基和弹性关节空间机器人的抗扰运动控制及基座和关节弹性振动同步抑制问题.在对基座和关节弹性进行等效线性弹簧假设的基础上,建立了弹性基和弹性关节空间机器人的动力学方程,并推导了基于等效刚度思想的奇异摄动慢、快变子系统.对传统参数自适应控制律进行σ修正并与鲁棒抗扰控制相结合,对不确定参数和有界外部扰动影响下的慢变子系统提出了基座姿态和臂杆关节刚性运动轨迹跟踪的改进自适应鲁棒抗扰控制方案.使用高增益线性状态观测器对快变高阶量进行实时观测,针对快变子系统设计了基座和关节弹性振动同步抑制的改进最优控制方案.仿真示例分析,表明了所提混合控制方案在空间机器人抗扰运动控制及振动抑制上的有效性.     

基于无源性理论的柔性关节控制器设计

为了提高柔性关节机器人的轨迹跟踪精度和抖动抑制能力,设计了一种基于无源性理论的柔性关节控制器.通过Simulink仿真验证和简化了该控制器,使其更加适合于多自由度机器人的控制.用于实验的7自由度机器人采用DSP+FPGA结构,数字信号处理器(DSP)和现场可编程逻辑门阵列(FPGA)分别实现非线性部分和线性部分计算,避免了由于自由度的增加引起的关节控制器性能变化.实验结果表明,与传统的PD控制相比,基于无源性理论的柔性关节控制器具有力矩波动小以及抖动抑制快、稳态精度高等优点.     

一种欠驱动柔性机器人模型的建立及控制

采用Lagrange建模方法建立了欠驱动柔性自平衡机器人的数学模型,对柔性关节部分考虑了其弹性势能,仿真验证了模型的正确性,刚度越大,机器人上半身角度跟踪越快.采用线性二次型最优控制有效地控制了柔性机器人的平衡问题,通过实验,验证了在状态不完全可观测情况下系统的可控性,实验表明,只需机器人上半身部分安装传感器即可控制机器人达到平衡状态.对机器人结构的设计提供了参考.     

面向足式机器人的新型可调刚度柔性关节的设计及性能测试

为提高足式机器人的运动适应能力,为其设计了一款具有刚度连续调节功能的新型柔性旋转关节.通过研究杠杆机构输出刚度与传动比的对应关系,提出以变传动比杠杆机构作为核心部件进行可调刚度柔性关节的设计.文中对关节的结构以及关节驱动方式等进行了紧凑化设计,以满足足式机器人系统对体积及重量的要求.在设计中通过分析关节输出刚度系数与关节相关结构参数之间的关系,为关节输出刚度调节选择了较为敏感的参数调节范围,提高了刚度调节的灵敏性.在此基础上,通过开展机构运动学分析,确定了关节机构的理论刚度输出固有特性.关节样机测试表明,该调节机构能够实现关节输出刚度的调整和有效控制,该关节在结构设计以及功能方面均可以满足在足式机器人腿部结构中的应用需求.     

MACCEPA 2.0: compliant actuator used for energy efficient hopping robot Chobino1D

The MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator) is an electric actuator of which the compliance and equilibrium position are fully independently controllable and both are set by two dedicated servomotor. In this paper an improvement of the actuator is proposed where the torque-angle curve and consequently the stiffness-angle curve can be modified by choosing an appropriate shape of a profile disk, which replaces the lever arm of the original design. The actuator has a large joint angle, torque and stiffness range and these properties can be made beneficial for safe human robot interaction and the construction of energy efficient walking, hopping and running robots. The benefit of the ability to store and release energy is shown by the 1DOF hopping robot Chobino1D. The achieved hopping height is much higher compared to a configuration in which the same motor is used without a series elastic element. The stiffness of the actuator increases with deflection, more closely resembling the properties shown by elastic tissue in humans.     

基于柔性人机接口的人机协调运动控制方法

为改善基于力信息的人机协调运动中人机交互力,采用了在人机接口中设置弹性元件的方法,建立了具有柔性人机接口的人机交互力学模型。在已有鲁棒自适应阻抗控制方法的基础上进行改进,提出了一种基于柔性人机接口的自适应阻抗控制方法。此控制方法是对阻抗外环位置速度进行比例补偿,对力控制内环采用模糊PID (proportion integral differential)控制,实现改进自适应阻抗算法,从而提高了位置跟随精度,并有效减小了人机交互力。分析了人机接口中弹性元件对控制效果的影响,获得了不同刚度系数时,交互力控制效果和位置跟随精度。在此基础上,建立了试验系统,完成了试验。人机协调运动试验结果显示：应用柔性人机接口和改进后的控制方法具有更好的人机交互力控制效果。标准运动输入试验结果显示：改进后的控制方法具有更好的人机交互力控制效果和更高的位置跟随精度;人机交互力大小、位置跟踪准确性与人机接口刚度系数大小均成正比。     

基于GPR和深度强化学习的分层人机协作控制

提出了一种基于高斯过程回归与深度强化学习的分层人机协作控制方法,并以人机协作控制球杆系统为例检验该方法的高效性.主要贡献是:1)在模型未知的情况下,采用深度强化学习算法设计了一种有效的非线性次优控制策略,并将其作为顶层期望控制策略以引导分层人机协作控制过程,解决了传统控制方法无法直接应用于模型未知人机协作场景的问题; 2)针对分层人机协作过程中人未知和随机控制策略带来的不利影响,采用高斯过程回归拟合人体控制策略以建立机器人对人控制行为的认知模型,在减弱该不利影响的同时提升机器人在协作过程中的主动性,从而进一步提升协作效率; 3)利用所得认知模型和期望控制策略设计机器人末端速度的控制律,并通过实验对比验证了所提方法的有效性.     

A Series-elastic Robot for Back-pain Rehabilitation

This paper addresses the robot-assisted rehabilitation of back pain, an epidemic health problem affecting a large portion of the population. The design is composed of two springs in series connected to an end-effector via a pair of antagonistic cables. The spring and cable arrangement forms an elastic coupling from the actuator to the output shaft. An input-output torque model of the series-elastic mechanism is established and studied numerically. The study also illustrates the variation of the mechanism’s effective stiffness by changing the springs’ position. In addition, we built a prototype of the robotic mechanism and design experiments with a robotic manipulator to experimentally investigate its dynamic characteristics. The experimental results confirm the predicted elasticity between the input motion and the output torque at the end-effector. We also observe an agreement between the data generated by the torque model and data collected from the experiments. An experiment with a full-scale robot and a human subject is carried out to investigate the human-robot interaction and the mechanism behavior.

     

Adaptive tracking control of flexible‐joint manipulators without overparametrization

In this paper, an adaptive controller is designed for rigid‐link flexible‐joint robot manipulators based on link and actuator position measurements only. It is based on the adaptive integrator backstepping method and the link and actuator velocity filters are used to estimate the unknown velocity terms. Moreover, the proposed controller exploits the estimate of the joint stiffness matrix inverse to overcome the overparametrization problem, which has been a significant drawback in adaptive partial state feedback controllers. It achieves asymptotic tracking of link positions while keeping all states and signals bounded. The tracking capability of the presented method is shown through simulation results of one‐ and two‐link flexible joint manipulators. © 2004 Wiley Periodicals, Inc.     

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.     

Global Output Tracking Control of Flexible Joint Robots via Factorization of the Manipulator Mass Matrix

This paper investigates the problem of global output feedback tracking control of flexible joint robots. Despite the fact that only link position and actuator position are available from measurements, the proposed controller ensures that the link position globally tracks the desired trajectory while keeping all the remaining signals bounded. The controller development uses a partial state-feedback linearization technique combined with the integrator backstepping control design method whereas a filter and an observer are utilized to remove the requirement of link and actuator velocity measurements. Partial state-feedback linearization of robot dynamics is performed by factoring the manipulator mass matrix into a quadratic form involving an integrable root matrix. The applicability of the proposed general design methodology is illustrated by an example of flexible joint planar robots. Numerical results for a two-link flexible joint planar robot are also provided.