Design of an Optimal 4-bar Mechanism Based Gravity Balanced Leg Orthosis

In this paper we propose the design and control of a 4-bar mechanism based gravity balanced orthosis for providing gait training to persons with disability. Human leg joints have a varying instantaneous centre of rotation and hence 4-bar mechanisms have been used to actuate the orthosis joints. Human gait is first recorded using a vision system and the hip and knee joint trajectories extracted from the data. Optimal 4-bar mechanisms are then designed using a genetic algorithm that gives the smallest mechanism that can replicate the hip and knee joint trajectories accurately. The orthosis joints are gravity balanced so that the potential energy of the system in any orientations is constant, and the wearer does not feel the weight of the system. Experimental and simulation results prove that the exoskeleton can effectively model the changing centre of rotation of the hip and knee joints and follow the desired human trajectories.     

理疗师交互下的下肢康复训练机器人个性化步态规划方法

针对偏瘫患者的个体差异及病况差异,提出了一种理疗师交互下的下肢康复训练机器人步态规划方法,在理疗师-减重悬吊式康复训练机器人-患者三者共存的复杂环境中,理疗师穿戴主控外骨骼直接行走实现步态时空参数规划,并融入理疗师的医学经验及对患者的评估.首先,基于旋量理论建立运动学模型,实现理疗师空间与机器人空间的运动映射;然后,统一规划机器人关节运动轨迹、减重机构重心调整轨迹及跑步机步速.最后,通过理疗师步态参数的实时采集、运动映射实验及机器人轨迹跟踪实验,验证了步态时空规划方法的有效性.结果表明,髋、膝关节规划角度在人体关节活动范围内,速度变化平稳,关节轨迹规划和重心调整规划均符合人体行走的生理特性.理疗师的参与实现了渐进康复训练中的个性化步态规划.     

Stroke Survivors' Gait Adaptations to a Powered Ankle–Foot Orthosis

Stroke is the leading cause of long-term disability in the US, and for many it causes loss of gait function. The purpose of this research is to examine stroke survivors' gait adaptations to training on the powered ankle–foot orthosis (PAFO). Of particular interest is the stroke survivors' ability to learn how to store and release energy properly while using the device. The PAFO utilizes robotic tendon technology and supports motion with a single degree of freedom — ankle rotation in the sagittal plane. This actuator comprises a motor and series spring. The user interacts with the output side of the spring while the robot controls the input side of the spring such that typical able-body ankle moments would be generated, assuming able-body ankle kinematics are seen at the output side of the spring. Three individuals post-stroke participated in a 3-week training protocol. Outcome measures (temporal, kinematic and kinetic) were derived from robot sensors and recorded for every step. These data are used to evaluate each stroke survivor's adaptations to robotic gait assistance. The robot was worn only on the paretic ankle. For validation of the kinematic results, motion capture data were collected on the third subject. All subjects showed increased cadence, ankle range of motion and power generation capabilities. Additionally, all subjects were able to achieve a larger power output than power input from the robot. Motion capture data collected from Subject 3 validated the robot sensor kinematic data on the affected side, but also demonstrated an unexpected gait adaptation on the unaffected ankle. Sensors on the gait-assisting robot provide large volumes of valuable information on how gait parameters change over time. We have developed key gait evaluation metrics based on the available robot sensor information that may be useful to future researchers. All subjects adapted their gait to the robotic assistance and many of their key metrics moved closer to typical able-body values. This suggests that each subject learned to utilize the assistive moments generated by the robot, despite having no predefined ankle trajectory input from the robot. The security of being harnessed on the treadmill led to more dramatic and favorable results.     

基于一阶不确定项估计律的机械臂鲁棒控制研究

针对多自由度机械臂控制系统的模型参数误差、关节摩擦力以及外部输入扰动等不确定项,设计了一类一阶误差估计律;结合基于机构动力学名义模型的输入输出反馈线性化控制算法,对六自由度刚性机械臂的时变轨迹跟踪控制进行了研究,理论上证明了设计的鲁棒控制器是全局渐进稳定的.仿真结果表明该控制策略对系统的各类不确定项具有很好的鲁棒性,能够实现高精度的轨迹跟踪控制.     

Coordinated path tracking of two vision-guided tractors for heavy-duty robotic vehicles

Heavy-duty robotic vehicles are increasingly required to transport and position large-quantity and large-scale objects in the manufacturing process. The load-carrying capacity of vehicles can be enhanced by configuring multiple automated guided tractors and coordinating their motions. A vision-guided tractor is developed by using an on-board camera in order to improve the guidance accuracy. Each tractor can recognize the guide paths, measure its path deviations and control the speeds of driving motors independently. A coordinated path tracking technique is proposed for two vision-guided tractors of a robotic vehicle, in order to make them move along a guide path accurately and smoothly. The finite area of the vision field, the actuation capacity of driving motors and the motion conflict of two tractors are considered as control constraints of path tracking. Six path deviation states and their relevant approaching trajectories are classified based on deviation properties and control constraints. The general law of mutual conversion of six approaching trajectories is analyzed. Other approaching trajectories should be converted into the tangent-arc trajectory that can eliminate two path deviations synchronously. Expected tracking distance is a coupled parameter that influences control efficiency, safety margin of trajectory conversion and coordination degree of two tractors. A fuzzy logic regulator is used in the leader-follower control strategy to adjust this parameter, by taking two path deviations and the difference of attitude angles of two tractors into account. Numerical simulation and prototype experiment show that two vision-guided tractors can move along the straight and curvilinear guide paths with high tracking accuracy, control efficiency and motion coordination, which enhanced the load-carrying capacity of robotic vehicles significantly.     

Initial System Evaluation of an Overground Rehabilitation Gait Training Robot (NaTUre-gaits)

For decades, robotic devices have been suggested to enhance motor recovery by replicating clinical manual-assisted training. This paper presents an overground gait rehabilitation robot, which consists of a pair of robotic orthoses, the connected pelvic arm in parallel and a mounted mobile platform. The overground walking incorporates pelvic control together with active joints on the lower limb. As a preliminary evaluation, system trials have been conducted on healthy subjects and a spinal cord injury (SCI) subject, respectively. Electromyography signals were recorded from muscles of the lower limb for each subject. Three experiments were carried out: (i) health volunteers walking at self-preferred walking speed, (ii) a SCI subject walking with the help of three helpers and (iii) the same SCI subject walking with the assistance provided by the gait device. In the experiment, the muscle activation of overground walking was compared between the manual-assisted and robotic-assisted methods. The initial results show that the performance of the device can provide impact-less overground walking and it is comparable to the performance obtained by manual assistance in gait rehabilitation training.     

Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors

This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.     

Adaptive control of an actuated ankle foot orthosis for paretic patients

This paper deals with the control of an active ankle foot orthosis (AAFO) to assist the gait of paretic patients. The AAFO system is driven by both, the residual human torque delivered by the muscles spanning the ankle joint and the AAFO’s actuator’s torque. A model reference adaptive control is proposed to assist dorsiflexion and plantar-flexion movements of the ankle joint during level walking. Unlike most classical model-based controllers, the proposed one does not require any prior estimation of the system’s (AAFO-wearer) parameters. The ankle reference trajectory is updated online based on the main gait cycle events and is adapted with respect to the self-selected speed of the wearer. The adaptive desired ankle trajectory is estimated using cubic spline interpolations between the different key events of the gait cycle. The closed-loop input-to-state stability of the AAFO-wearer system with respect to a bounded human muscular torque is proved by a Lyapunov analysis. Experimental results obtained from three healthy subjects and one paretic patient, show satisfactory results in terms of tracking performance and ankle assistance throughout the full gait cycle. The experiments also show good performance at different walking speeds and with different gait sub-phase duration proportions.     

Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit-to-stand assistance

This paper presents a novel control approach for a knee exoskeleton to assist individuals with lower extremity weakness during sit-to-stand motion. The proposed method consists of a trajectory generator and an impedance controller. The trajectory generator uses a library of sample trajectories as the training data and the initial joint angles as the input to predict the user’s intended sit-to-stand trajectory. Utilizing the dynamic movement primitives theory, the trajectory generator represents the predicted trajectory in a time-normalized and rather a flexible framework. The impedance controller is then employed to provide assistance by guiding the knee joint to move along the predicted trajectory. Moreover, the human-exoskeleton interaction force is used as the feedback for on-line adaptation of the trajectory speed. The proposed control strategy was tested on a healthy adult who wore the knee exoskeleton on his leg. The subject was asked to perform a number of sit-to-stand movements from different sitting positions. Next, the measured data and the inverse dynamic model of the human-exoskeleton system are used to calculate the knee power and torque profiles. The results reveal that average muscle activity decreases when the subject is assisted by the exoskeleton.     

Stability and control aspects of flexible link robot manipulators during constrained motion tasks

The effect of robotic manipulator structural compliance on system stability and trajectory tracking performance and the compensation of this structural compliance has been the subject of a number of publications for the case of robotic manipulator noncontact task execution. The subject of this article is the examination of dynamics and stability issues of a robotic manipulator modeled with link structural flexibility during execution of a task that requires the robot tip to contact fixed rigid objects in the work environment. The dynamic behavior of a general n degree of freedom flexible link manipulator is investigated with a previously proposed nonlinear computed torque constrained motion control applied, computed based on the rigid link equations of motion. Through the use of techniques from the theory of singular perturbations, the analysis of the system stability is investigated by examining the stability of the “slow” and “fast” subsystem dynamics. The conditions under which the fast subsystem dynamics exhibit a stable response are examined. It is shown that if certain conditions are satisfied a control based on only the rigid link equations of motion will lead to asymptotic trajectory tracking of the desired generalized position and force trajectories during constrained motion. Experiments reported here have been carried out to investigate the performance of the nonlinear computed torque control law during constrained motion of the manipulator. While based only on the rigid link equations of motion, experimental results confirm that high-frequency structural link modes, exhibited in the response of the robot, are asymptotically stable and do not destabilize the slow subsystem dynamics, leading to asymptotic trajectory tracking of the overall system. © 1992 John Wiley & Sons, Inc.     

Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification

We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.     

基于中枢模式发生器的仿生机器狗步态控制的应用研究

由于人工规划产生的步态是比较僵硬的、缓慢的,缺乏灵活的自组织能力,与真正的动物步态存在很大差别;文章提出了机器狗生物步态的概念;以生物的中枢模式发生器CPG模型为核心建立仿生四足机器狗运动控制系统;根据哺乳动物的肢体运动关系,建立机器狗膝髋关节运动关系方程,并设计系统软硬件;设计的控制器能够有效地克服机器狗关节轨迹跟踪控制中耦合、力矩非线性等因素的影响,且具有自适应能力;通过仿真验证了应用于机器狗的生物CPG控制机理的控制方法是有效的。     

Accurate tracking of legged robots on natural terrain

Statically stable walking locomotion research has focused mainly on robot design and gait generation. However, there is a need to expand robots’ capabilities so that walking machines can accomplish the kinds of real tasks for which they are eminently suited. Many such tasks demand trajectory tracking, but researchers have traditionally ignored this subject. This article focuses on the tracking of predefined trajectories with hexapod robots walking on natural terrain with forbidden zones. The method presented herein, which relies on gait algorithms defined elsewhere, describes certain localization strategies and control techniques that have been employed to follow trajectories accurately and have been implemented in a real walking hexapod. Several experimental examples are included to assess the proposed algorithms.     

Prediction accuracy in estimating joint angle trajectories using a video posture coding method for sagittal lifting tasks

This study investigated prediction accuracy of a video posture coding method for lifting joint trajectory estimation. From three filming angles, the coder selected four key snapshots, identified joint angles and then a prediction program estimated the joint trajectories over the course of a lift. Results revealed a limited range of differences of joint angles (elbow, shoulder, hip, knee, ankle) between the manual coding method and the electromagnetic motion tracking system approach. Lifting range significantly affected estimate accuracy for all joints and camcorder filming angle had a significant effect on all joints but the hip. Joint trajectory predictions were more accurate for knuckle-to-shoulder lifts than for floor-to-shoulder or floor-to-knuckle lifts with average root mean square errors (RMSE) of 8.65°, 11.15° and 11.93°, respectively. Accuracy was also greater for the filming angles orthogonal to the participant's sagittal plane (RMSE = 9.97°) as compared to filming angles of 45° (RMSE = 11.01°) or 135° (10.71°). The effects of lifting speed and loading conditions were minimal. To further increase prediction accuracy, improved prediction algorithms and/or better posture matching methods should be investigated.

Statement of Relevance: Observation and classification of postures are common steps in risk assessment of manual materials handling tasks. The ability to accurately predict lifting patterns through video coding can provide ergonomists with greater resolution in characterising or assessing the lifting tasks than evaluation based solely on sampling with a single lifting posture event.     

Clock-turning gait synthesis for humanoid robots

Turning gait is a basic motion for humanoid robots. This paper presents a method for humanoid tuming, i.e. clock-turning. The objective of clock-turning is to change robot direction at a stationary spot. The clock-turning planning consists of four steps： ankle trajectory generation, hip trajectory generation, knee trajectory generation, and inverse kinematics calculation. Our proposed method is based on a typical humanoid structure with 12 DOFs （degrees of freedom）. The final output of clock-turning planning is 12 reference trajectories, which are used to control a humanoid robot with 12 DOFs. ZMP （zero moment point） is used as stability criterion for the planning. Simulation experiments are conducted to verify the effectiveness of our proposed clock-turuing method.     

Modeling and simulation of powered hip orthosis by pneumatic actuators

In this study, a simulation model for a powered hip orthosis (PHO) with air muscles to predict the gait of paraplegics is presented which can be used as a design tool for hip orthoses. Before simulation, mathematical models for a human dummy with an orthosis and a pneumatic muscle actuator were generated. For the air muscle, coefficients required were obtained by static and dynamic experiments of the air muscle and experiments for the valve controlling the air pressure. The computation was conducted on the ADAMS package together with MATLAB. Computer simulation of the flexion of hip joints by the pneumatic muscle results in similar values to those from gait analysis. With the development of a simulation model for a PHO, the gait simulation model using pneumatic muscles can be used to analyze and evaluate the characteristics and efficiency of a PHO by setting the input and boundary conditions.     

超前采样时间迭代学习的下肢康复机器人轨迹跟踪控制

为了实现下肢康复机器人在康复训练过程中高精度的末端轨迹跟踪控制,提出了一种利用超前采样时间的鲁棒自适应迭代学习控制方法。所述超前采样时间迭代算法,是指利用之前运行批次在t+Δ采样时刻的髋膝关节力矩输出,优化调整下一次运行时刻t处的关节力矩给定。仿真结果表明,采用超前采样时间迭代控制,末端轨迹误差具有更快的收敛速度和跟踪精度,并且具有较好的抗干扰性能。     

Joint Motion Control of a Powered Lower Limb Orthosis for Rehabilitation

Many patients with spinal injures are confined to wheelchairs, leading to a sedentary lifestyle with secondary pathologies and increased dependence on a carer. Increasing evidence has shown that locomotor training reduces the incidence of these secondary pathologies, but the physical effort involved in this training is such that there is poor compliance. This paper reports on the design and control of a new "human friendly" orthosis (exoskeleton), powered by high power pneumatic Muscle Actuators (pMAs). The combination of a highly compliant actuation system, with an intelligent embedded control mechanism which senses hip, knee, and ankle positions, velocity, acceleration and force, produces powerful yet inherently safe operation for paraplegic patients. This paper analyzes the motion of ankle, knee, and hip joints under zero loading, and loads which simulate human limb mass, showing that the use of "soft" actuators can provide a smooth user friendly motion. The application of this technology will greatly improve the rehabilitative protocols for paraplegic patients.     

Intelligent control based on wavelet decomposition and neural network for predicting of human trajectories with a novel vision-based robotic

In this paper, an intelligent novel vision-based robotic tracking model is developed to predict the performance of human trajectories with a novel vision-based robotic tracking system. The developed model is based on wavelet packet decomposition, entropy and neural network. We represent an implementation of a novel vision-based robotic tracking system based on wavelet decomposition and artificial neural (WD-ANN) which can track desired human trajectory pattern in real environments. The input–output data set of the novel vision-based robotic tracking system were first stored and than these data sets were used to predict the robotic tracking based on WD-ANN. In simulations, performance measures were obtained to compare the predicted and human–robot trajectories like actual values for model validation. In statistical analysis, the RMS value is 0.0729 and the R² value is 99.76% for the WD-ANN model. This study shows that the values predicted with the WD-ANN can be used to predict human trajectory by vision-based robotic tracking system quite accurately. All simulations have shown that the proposed method is more effective and controls the systems quite successful.