Powered knee prosthesis commonly improves locomotion of above-knee amputees by generating net positive mechanical work at the knee joint which is especially required for movements with active knee extension and flexion such as sit-to-stand maneuvers, level-ground walking with various walking speed, stair/slope ascent ambulation and so forth. These studies tend to refer and trace normal human locomotion, and have amputees move “normally”, which is quite difficult for patients with severe disability such as four-limb deficiency. In this study, a powered knee prosthesis with small-scale, light-weight and affordable series elastic actuator (PKP-SEA) is developed and adopted to a walking rehabilitation of a patient with four-limb deficiency. A subject is 45 years old and has no experience to walk on prosthetic devices in his life. During the experiment, walking performance is investigated by turning PKP-SEA control parameters. As a result, the PKP-SEA shows capability of improving his walking performance in terms of walking speed and consistency not by replicating normal human walking, but suggesting an original walking gait based on his body condition and remaining functionality.

In this paper, we study the mechanical behavior of leg muscles and tendons during human walking in order to motivate the design of economical robotic legs. We hypothesize that quasi-passive, series-elastic clutch units spanning the knee joint in a musculoskeletal arrangement can capture the dominant mechanical behaviors of the human knee in level-ground walking. Since the mechanical work done by the knee joint throughout a level-ground self-selected-speed walking cycle is negative, and since there is no element capable of dissipating mechanical energy in the musculoskeletal model, biarticular elements would necessarily need to transfer energy from the knee joint to hip and/or ankle joints. This mechanism would reduce the necessary actuator work and improve the mechanical economy of a human-like walking robot. As a preliminary evaluation of these hypotheses, we vary model parameters, or spring constants and clutch engagement times, using an optimization scheme that minimizes ankle and hip actuator positive work while still maintaining human-like knee mechanics. For model evaluation, kinetic and kinematic gait data were employed from one study participant walking across a level-ground surface at a self-selected gait speed. With this under-actuated leg model, we find good agreement between the model's quasi-passive knee torque and experimental knee values, suggesting that a knee actuator is not necessary for level-ground robotic ambulation at self-selected gait speeds.

In this paper, we present an under-actuated model of human walking, comprising only a soleus muscle and flexion/extension monoarticular hip muscles. The remaining muscle groups of the human leg are modeled using quasi-passive, series-elastic clutch elements. We hypothesize that series-elastic clutch units spanning the knee joint in a musculoskeletal arrangement can capture the dominant mechanical behaviors of the human knee in level-ground walking. As an evaluation of the musculoskeletal model, we vary model parameters, or spring constants, and muscle control parameters using an optimization scheme that maximizes walking distance and minimizes the mechanical economy of walking. We used a positive force feedback reflex control for the model's soleus muscle, and upper body position control for the hip muscles. The model's clutches were engaged/disengaged using simple state machine controllers. For model evaluation, a forward dynamics simulation was conducted, and the resulting mechanics were compared to human walking data. The model makes qualitative predictions of joint mechanics, electromyography and mechanical economy.

In this paper, we seek to understand how leg muscles and tendons work mechanically during walking in order to motivate the design of efficient robotic legs. We hypothesize that a robotic leg comprising only knee and ankle passive and quasi-passive elements, including springs, clutches and variable-damping components, can capture the dominant mechanical behavior of the human knee and ankle during level-ground walking at self-selected speeds. As a preliminary evaluation of this hypothesis, we put forth a simple leg model that captures the gross features of the human leg musculoskeletal architecture. We vary model parameters, or spring constants, damping levels and times when clutches are engaged, using an optimization scheme where errors between model joint behaviours and biological joint mechanics are minimized. For model evaluation, kinetic and kinematic gait data are employed from a single participant walking across a level-ground surface at a self-selected gait speed (1.3 m/sec). With only a single hip actuator, we find good agreement between model predictions and experimental gait data, suggesting that knee and ankle actuators are not necessary for level-ground robotic ambulation at self-selected gait speeds. This result is in support of the idea that muscles that span the human knee and ankle mainly operate eccentrically or isometrically, affording the relatively high metabolic walking economy of humans

In this paper, a method for co-evolving morphology and controller of a real biped walking robot is proposed using evolutionary computation. Although certain control theories exist, such as zero moment point (ZMP) compensation, these theories assume humanoid robot morphology is given in advance. Thus, engineers have to design a control program for a priori designed morphology. If morphology and locomotion are considered simultaneously, we do not have to spend time with trial-and-error. The detailed model of morphology is used for morphology in which the characteristics of the servo module are considered. As the controller, pattern generators are used. The biped walking robot which can walk the longest distance are designed with a two step genetic algorithm. As a result, the detailed model of the robot which can walk the longest distance is generated from limited size of chromosomes.

In this paper, evolutionary designing method of a biped walking robot is proposed and evaluated in comparison with traditional manner. Usually, the structure and control system of robot have been designed separately. Therefore, the structure is given in advance when the control system is developed. This results in a great deal of trial-and-errors. On the other hand, evolutionary designing method carries out these operations simultaneously. Two calculations are conducted of which morphologies are changed (evolutionary designing method) and not changed (traditional manner) during the evolution. As a result, almost the same number of pareto optimal solutions are emerged. However, solutions generated with evolutionary designing method obtain higher fitnesses than the others. This means that evolutionary designing method could be powerful and efficient method for the real robot.

A method of stabilizing the biped locomotion in real-time is necessary to achieve biped locomotion that is stable and practical. We propose a new walk stabilization technique. First, we define the ZMP stability index. The proposed method compensate ZMP in real-time to control of inertia force and adjust feedback parameters according to the stability index. This results in altering the speed of walk instead of the walk pattern. As a result, we can compensate ZMP without changing the walking pattern and achieve both of the robustness against disturbances and high responsiveness for ZMP error correction. In this paper, after describing the proposed real-tune ZMP compensation method, the validity of the proposed method is tested using humanoid robot named 'morph3' that is created in our laboratory. Verification experiments, including simulation are performed and their results prove the validity of the proposed method.

We present a method for co-evolving structures and a controller of biped walking robots. Currently, biped walking humanoid robots are designed manually on trial-and-error basis. Although a certain control theory exists, such as zero moment point compensation, these theories assume humanoid robot morphology is given in advance. Thus, engineers have to design a control program for a priori designed morphology. It is very time consuming if both morphology and locomotion are considered simultaneously. Therefore, a method useful for designing the robot is proposed. First, simple models for both morphology and controller are used for the dynamic simulation, which are multi-link models as morphology and two kinds of controllers. One is a layered neural network and the other is a neural oscillator. Robots with optimal energy efficiency of walking are designed with the genetic algorithm. As a result, various combinations of morphologies and gaits are generated, and the relationship between the length of each link and moving distance is obtained, which gives an optimal energy efficiency. Moreover, the robots are encoded from the limited size of chromosomes.

A new type of master-slave control methodology, which has the merits of both unilateral and bilateral ones, is proposed. The methodology is built on switching the unilateral feedback controls of position and force as required using switching and elastic elements. Proposed methodology not only eliminates the demerits of bilateral control, but also supplies the mechanical force feedback to the operator. It utilizes a feature of human factor that direct displacement feedback is not as important as visual and force feedback. Effectiveness of the proposed method is confirmed by experiments using a developed simple single axis master-slave arm system. Driving test of the experimental devices and sensory evaluations are conducted. As a result, it is confirmed that the methodology successfully provides the sense of touch to the operator of the system.

This paper presents a method for humanoid walking acquisition with less energy consumption based on a two-stage genetic algorithm. In the first phase of genetic algorithm, in order to acquire the continuous walking motion, the fitness function consists of a walking distance (longer is better). In the second phase, the fitness function consists of a walking distance (longer) and energy consumption (less) for acquisition of highly energy-efficient walking. Further, we restrain the relationship among some joints and keep knee joint straight on supporting leg in order to ensure the less energy consumption. We apply the method to our platform PINO which has low-torque actuators owing to the servo modules. In order to realize a genetic process, we encode the scaling parameter of the joint movements and the phase difference between the joints into the computational simulation which considers the characteristics of the servo module used in our platform, PINO. The evolved results are applied to a real PINO and its smooth and stable walking with less energy consumption is verified.

In this paper, we present a method for co-evolving morphology and controller of bi-ped humanoid robots. Currently, bi-ped walking humanoid robots are designed manually on trial-and-error basis. Thus, engineers have to design control program for apriori designed morphology, neither of them shown to be optimal within a large design space. We propose evolutionary approach that co-evolves the morphology and controller. Two steps of co-evolutions were achieved in a precision dynamics simulator. One is for obtaining the optimal lengths of links using simple multi-link model and the other is for obtaining the optimal geometry of servomotor which is often used for radio control car. Finally, unexpected optimal solutions were discovered. This indicates that a complex design task of biped humanoid can be performed automatically using evolution-based approach, thus varieties of humanoid robots can be designed in speedy manner.

In this paper, morphology of body and neural systems that define the locomotion of multilinked locomotive robots that can adapt to changes in environment are designed using the evolutionary computation. The morphology of the body and neural systems have a close relationship to each other. The model of the robot is designed so that the morphology of the body and neural systems emerge simultaneously. The morphology of the body and neural systems are generated using a genetic programming. The tasks are that the robots move on ground including hills of different heights in the two dimensional lateral simulated world under the effect of gravity. The robots are evaluated based both on a moving distance and an efficiency. As a result, various combinations between the morphology of the body and neural systems of the robots were emerged. The evolved robots were able to go over hills which they had not experienced.