This paper presents an adjustable knee joint for a wearable robot for the elderly, to provide physical gait assistance. In order to compensate for the translational and rotational movements of the knee in the sagittal plane as well as aligning the frontal plane, the proposed knee joint is implemented with a rolling joint, two aligning passive joints, four-bar linkage, and a sliding mechanism. Because the sliding mechanism, four-bar linkage, and rolling joint exist between the actuator and the joint mechanism, the rotation angle of the actuator and the flexion angle of the rolling joint do not change equally due to the kinematic characteristics. In order to define the flexion angle change of the rolling joint with respect to the change to the actuator angle in the flexion/extension motion of knee, the design parameters and flexion model are proposed in this paper. The proposed artificial knee joint effectively delivers the torque required for assistance while adapting to the joint motion of the wearer. This paper describes the mechanical design of this knee mechanism and its implementation on a wearable robot and in preliminary experiments. The performance of the proposed mechanism was verified by simulations and experiments. Finally, preliminary experiments were performed to demonstrate the possibility of reducing the metabolic cost of using a knee assist.

In this paper, we propose a new and simple control strategy for gait assistance with a hip exoskeleton robot. This controller is based on the time delayed, self-feedback known for stabilizing oscillatory systems under certain conditions. In this controller, there are no separate estimators for the gait phase nor the environment, yet the controller can be generalized to operate under various walking conditions (e.g., stair and ramp walking). We first define a state variable representing the current leg's movement with hip joint angles. A simple assistance control can be described in closed-loop form with the delayed state feedback. By assigning the appropriate time-delay and self-feedback gain, we can generate assistive torques stably under the interaction between human and exoskeleton. The controller provides immediate and smooth assistance to user movement by reflecting the change of leg motion at every control period. The proposed joint-angle-based delayed-feedback assistance controller can operate under various walking speeds and environmental changes (e.g., stairs and ramps) without the need for additional sensors, computational processing, and parameter adjustment. Using a simple leg swing model, we perform a stability analysis under a simplified condition to provide insights into the effects of the time-delayed feedback in oscillatory systems. Then, we experimentally validate the efficacy of the proposed assistance controller by measuring the metabolic energy expenditure for level treadmill walking. We also test and analyze the generated assistive torques and power under the different walking conditions to show the generalizability of the controller.

We developed a robotic lower-limb exoskeleton for those who have weakened muscle due to aging and experience difficulty in walking or getting up without help. The exoskeleton covering both limbs from the feet to the waist has 6 electric actuators in the hip abduction/adduction, hip extension/flexion and knee extension/flexion joints. For users with volitional motion, delivering assistance power according to their intention is a challenging task. We propose an adaptive oscillator-based controller to assist users walk in the lower-limb exoskeleton. To adapt to changes in walking speed and environment, motion command from the controller is modulated by estimate walking speed and walking environment recognized as one of the following categories: level ground, stairs up/down and slope up/down. Experimental results demonstrate the feasibility of the proposed environment recognition method and the impact of assistance on the metabolic cost of walking on level and inclined treadmills.

We propose a simulation framework for gait assistance with ankle pathologies. We first construct the neu-romuscular walking model, then design the parameters for assistance torques for stance and swing legs. The parameter values are determined by performing dynamic optimizations which takes into account the human-exoskeleton interactive dynamics. The simulated energy expenditure and kinematic data are compared with the real data. Case studies involve abnormal gaits with 1) foot drop, 2) foot drop and plantarflexion failure. We evaluate the gait efficiency and walking speed for the different gait types. Our result shows that each gait type should have a different assistance strategy (timing and magnitude) compared to the assistance strategy of a normal gait.

We propose a novel two-tier gait recognizer using a minimal number of mechanical sensors built into a lower limb exoskeleton. The aim of this recognizer is to offer one-step selection of one of the actions ascending, descending, and level walking during five gaits (stair ascent/descent, slope ascent/descent, level walking). The proposed recognizer is executed at the moment of foot contact as estimated by an inertial measurement unit (IMU) on the pelvis without using direct foot sensors. The proposed recognizer selects the gait by using the relations between the angles formed by the hip and knee joints during the last step. As this study constitutes preliminary work for lower limb exoskeletons, we used a hip exoskeleton integrated with two wireless IMUs on the shank to set up the lower limb exoskeleton sensor configuration. Experiments were used to evaluate IMU-based foot contact estimation with respect to a foot sensor based on a force-sensitive resistor (FSR) for stairs, slopes, and level ground. In addition, we evaluated the performance of the proposed two-tier gait recognizer by using two healthy male subjects in various gait environments.

In this paper, we propose a novel assistance strategy for stair ascent walking using our robotic hip exoskeleton. Our strategy exploits foot contact event estimated by an inertial measurement unit (IMU) and can detect user intention as well as reflect user preference. In our strategy, a gait cycle is divided into 4 phases and the transitions between the phases are based on events that are unavoidable and can be detected reliably using the sensors available for our exoskeleton. The proposed strategy presents criteria for the initiation and termination of assistance by recognizing user intention, as well as methods to adjust the timing and the amount of assistance corresponding to user preference. When each step starts, the duration of assistance is determined and the torque profile for the whole step is planned ahead. We demonstrate the validity of the proposed strategy for stair ascent and examine its effects on hip joint angle trajectory through experimental results.

This paper presents a simulation framework for pathological gait assistance with a hip exoskeleton. Previously we had developed an event-driven controller for gait assistance [1]. We now simulate (or optimize) the gait assistance in ankle pathologies (e.g., weak dorsiflexion or plantarflexion). It is done by 1) utilizing the neuromuscular walking model, 2) parameterizing assistive torques for swing and stance legs, and 3) performing dynamic optimizations that takes into account the human-robot interactive dynamics. We evaluate the energy expenditures and walking parameters for the different gait types. Results show that each gait type should have a different assistance strategy comparing with the assistance of normal gait. Although we need further studies about the pathologies, our simulation model is feasible to design the gait assistance for the ankle muscle weaknesses.

In this paper, we propose a novel online gait task recognition algorithm for hip exoskeleton. The proposed algorithm provides an automatic and prompt recognition result in just one step based on the relations between both hip joint angles at the moment of foot contact. Gait task recognition is one of the challenges that walking assist devices must address to offer adaptable and reliable assistance to users. However gait task recognition in hip exoskeleton is challenging because the sensors are very limited and fast gait task recognition is required to prevent inadequate assistance and reduce fall risk. Although in general foot contact event can be considered as crucial information during walking, it has not received attention in hip exoskeletons with no sensors corresponding foot force or pressure. In this study, we exploit foot contact event as a critical point to perform gait task recognition in hip exoskeleton. The proposed algorithm suggests a foot contact estimation method without using any foot force or pressure sensors and a rule-based inference system to recognize a new gait task in real time. Results presented from experiments will demonstrate the validity and performance of the proposed algorithm.

This paper presents a control method for walking assist with hip-mounted exoskeleton robots. For modeling a user's current walking motion, a novel finite state machine is first constructed. We divide a walking cycle uniformly using the inevitable zero crossing events. When state transitions occur, we capture the current walking spatio-temporal sensor data as discrete form. By using the sensed hip data as boundary conditions, we also develop a gait primitives based motion reconstruction method. Gait primitives are a form of basis trajectories to represent various joint motions. From those methods we estimate the moment of heel landing with interpolated knee joint motions. Utilizing the user's previous opposite step motion, we predict the positive or negative work intervals of the current step motion. This makes it possible to achieve natural ‘one shot’ assist by driving adapted torques fast. This assist strategy is also effective to enhance gait regularity. The measures of stride time variability are improved by over 30% for the simulated experiment. Various real experimentations demonstrate the feasibility of our approach.

The differential drive mechanism, which is one of the mechanisms of wheeled mobile robots, is simple and useful for the motion of the mobile robot. The mechanism, however, has typical disadvantages of losing mobility, falling down, etc. when the robot moves over obstacles or uneven terrains. A novel suspension mechanism presented in this paper was designed to help the robot to overcome these problems. In particular, this mechanism is very suitable for a tall robot, which is susceptible to overturning because of the disturbance caused by acceleration, deceleration, and collision. The proposed mechanism called a Multilayered Suspension Mechanism is composed of the effective and well-directed combination of springs and dampers. It is very simple and cost-effective since it has no actuator for suspension. In this paper, mechanical construction and characteristics of the mechanism are described. Then, excellence and performance of the proposed mechanism are demonstrated by simulations and experiments.

We propose a control framework for dynamic bipedal locomotion with compliant joints. A novel 3D dynamic walking is achieved by utilizing natural dynamics of the system. It is done by 1) driving robot joints directly with the posture-based state machine and 2) controlling tendon-driven compliant actuators. To enlarge gait's basin attraction for stable walking, we also adaptively plan step-to-step motion and compensate stance/swing motion. Final joint input is described by a superposition of state machine control torques and compensation torques of balancers. Various walking styles are easily generated by composing straight and turning gait-primitives and such walking is effectively able to adapt on various environments. Our proposed method is applied to a torque controlled robot platform, Roboray. Experimental results show that gaits are able to traverse inclined and rough terrains with bounded variations, and the result gaits are human-like comparing the conventional knee bent walkers.

This paper presents a framework to generate dynamic walking for biped robots. A set of self-stable gait primitives is first constructed. It is done by 1) representing parametric gait primitives, 2) utilizing state-dependent torque control, and 3) doing numerical optimization that takes into account the complex multi-body dynamics with frictional contact forces. Dynamic walking to follow the arbitrary path including a curve is then generated online via sequentially composing primitive motions. Results show that dynamic gaits are humanlike and efficient compared to the conventional knee bent walkers. Our proposed method is applied to a torque-controlled, human-sized biped robot platform, `Roboray' which is cable-driven partially for joint compliance. Following a discussion on robot design and control, experimental results are also reported.

We propose a balancing control framework for a torque-controlled biped robot, Roboray. Roboray has two 6 DOF legs and torque sensors are integrated at all the leg joints. It has a new cable-driven joint module as a pitch joint drive, which is highly back-drivable and elastic. Using these hardware characteristics, we propose a new balancing control algorithm. This algorithm is the combination of gravity compensation, virtual gravity control and damping control. A friction compensation technique is also introduced in order to eliminate the nonlinearity of damping and to improve the performance of torque tracking. Our proposed method is applied to a simple inverted pendulum system and Roboray. Experimental results show that these two system keep their balance when they are pushed slightly.

We propose a framework for online generation of efficient gaits on uneven terrain. A set of dynamically optimal leg swing motion primitives for irregular terrains is first constructed offline-this is done by generating minimum torque motions for various starting and ending ground configurations, extracting dominant principal components, and forming basis functions. Gaits are then generated online via linear interpolation of the principal component basis functions, using a distance metric on SO(3) to select the components. The algorithm is verified via dynamic simulations involving the STriDER, a three legged passive-walking robot. Our results show that gaits using only knee-actuated leg swings are able to traverse uneven terrain of bounded variation.

This paper investigates the extent to which biarticular actuation mechanisms-antagonistic actuation schemes with spring stiffness that extend over two joints, similar in function to biarticular muscles found in legged animals-improve the performance of jumping and other fast explosive robot movements. Robust gradient-based optimization algorithms that take into account the dynamic properties and various contact and actuator constraints of biarticular systems are developed. We then quantitatively evaluate the gains in jumping vis-a-vis conventional joint actuation schemes. We also examine the effects of biarticular link stiffness and link mass distributions on the jumping performance of the biarticular mechanism.

We propose a framework for robot movement coordination and learning that combines elements of movement storage, dynamic models, and optimization, with the ultimate objective of efficiently generating natural, human-like motions. One of the novel features of our approach is that each movement primitive is represented and stored as a set of joint trajectory basis functions; these basis functions are extracted via a principal component analysis of human motion capture data. By representing arbitrary movements as a linear combination of these basis functions, and by taking advantage of recently developed geometric optimization algorithms for multibody systems, dynamics-based optimization can be more efficiently performed. Case studies with a diverse set of arm movements demonstrate the feasibility of our approach.