The development and control of a flexible-spine for a human-form robot

In this paper, the development of a robot which has a flexible spine is presented. By embedding a multi-d.o.f. soft structure into a robot body as a spine, the robot can increase its ability to absorb shock and to work in various environment such as narrow places. As a result of these abilities, the robot can expand its opportunity to work in the human environment. Moreover, its motion could be more natural. The developed full-body human-form robot has a five-jointed flexible spine. Each joint (vertebra) has 3 d.o.f. Between each vertebrae is a 'disk' made of silicone rubber. The spine is controlled by eight tendons, whose tensions can be controlled using tension sensors and locally distributed microcontrollers. This paper describes the development of the flexible spine and the control of the posture of the spine and body.     

Parkinson's disease: a motor control study using a wrist robot

Deep brain stimulation (DBS) is the most common surgical procedure for patients with Parkinson's disease (PD). DBS has been shown to have a positive effect on PD symptoms; however, its specific effects on motor control are not yet understood. We introduce the novel use of a wrist robot in studying the effects of stimulation on motor performance and learning. We present results from patients performing reaching movements in a null field and in a force field with and without stimulation. We discuss special cases where robotic testing reveals otherwise undiagnosed impairments, and where clinical scores and robot-based scores display opposing trends.     

Synthesized articulated behavior using space-temporal on-line principal component analysis

This paper presents a new framework to synthesize humanoid behavior by learning and imitating the behavior of an articulated body using motion capture. The video-based motion capturing method has been developed mainly for analysis of human movement, but is very rarely used to teach or imitate the behavior of an articulated body to a virtual agent in an on-line manner. Using our proposed applications, new behaviors of one agent can be simultaneously analyzed and used to train or imitate another with a novel visual learning methodology. In the on-line learning phase, we propose a new way of synthesizing humanoid behavior based on on-line learning of principal component analysis (PCA) bases of the behavior. Although there are many existing studies which utilize PCA for object/behavior representation, this paper introduces two criteria to determine if the dimension of the subspace is to be expanded or not and applies a Fisher criterion to synthesize new behaviors. The proposed methodology is well-matched to both behavioral training and synthesis, since it is automatically carried out as an on-line long-term learning of humanoid behaviors without the overhead of an expanding learning space. The final outcome of these methodologies is to synthesize multiple humanoid behaviors for the generation of arbitrary behaviors. The experimental results using a humanoid figure and a virtual robot demonstrate the feasibility and merits of this method.     

SLAMMOT-SP: Simultaneous SLAMMOT and Scene Prediction

In recent years, SLAMMOT (simultaneous localization, mapping and moving object tracking) has attracted widespread attention in the mobile robot field. This paper proposes a new approach, SLAMMOT-SP, which combines SLAMMOT and scene prediction (SP). It extends the SLAMMOT problem to simultaneous map prediction and moving object trajectory prediction. The robot not only passively collects the data and executes SLAMMOT, but actively predicts the scene. The recursive Bayesian formulation of SLAMMOT-SP is derived for real-time operations. A generalized framework for tracking and predicting the moving objects is also proposed. Simulations and experiments show that the proposed SLAMMOT-SP is effective and can be performed in real-time.     

A study of an autonomous mobile robot in an outdoor environment

This paper describes an experimental study of the fabrication of micro-mechanisms on a silicon wafer. Planar process technology developed in the industry of CMOS LSI was employed. The structural material is CVD-polycrystalline silicon with a thickness of 2.5 μm and the sacrificial material is CVD-SiO₂ with a thickness of 1.0 μm. In the experimental study, micro-rotors with a shaft and a cap in an assembled form were fabricated on a silicon wafer. The self-alignment process gave a tolerance of 1.0 μm between the rotor and the shaft. The maximum rotation speed observed was 9 x 10⁴ rpm by blowing nitrogen gas.     

Closed-Form Inverse Kinematics for Continuum Manipulators

This paper presents a novel, analytical approach to solving inverse kinematics for multi-section continuum robots, defined as robots composed of a continuously bendable backbone. The problem is decomposed into several simpler subproblems. First, this paper presents a solution to the inverse kinematics problem for a single-section trunk. Assuming endpoints for all sections of a multi-section trunk are known, this paper then details applying single-section inverse kinematics to each section of the multi-section trunk by compensating for the resulting changes in orientation. Finally, an approach which computes per-section endpoints given only a final-section endpoint provides a complete solution to the multi-section inverse kinematics problem. The results of implementing these algorithms in simulation and on a prototype continuum robot are presented and possible applications discussed.     

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

A robot test-bed for assistance and assessment in physical therapy

This paper describes an experimental test-bed that was developed to assist and assess rehabilitation during physical and occupational therapy. A PUMA 260 robot was used with a controller and interface software developed in-house. The robot is designed to operate in two modes: (1) passive and (2) active. In the passive mode, the robot moves the subject's arm through specified paths. In the active mode, a subject guides the robot along a predefined path overcoming specified joint stiffness. In this mode, the controller compensates for gravity so that the robot can support its own weight in an arbitrary configuration. The developed graphical interface enables display of the current configuration of the robot in real-time, and allows us to customize experiments for a subject, and collect force and position data during an experiment. The results of a preliminary study with healthy subjects using this test-bed are also presented along with issues involved in the choice of paths and interpretation of the results.     

Control of giant swing motion of a two-link horizontal bar gymnastic robot

We investigated a control method to realize the three different types of free giant swing motions produced by a two-link horizontal bar gymnastic robot. By evaluating the eigenvalues of the transitional error matrix on the Poincare plane, it was found that the stable giant swing motions could be obtained by a proposed configuration control, in which the actuated joint torque is controlled such that the measured state variables follow the reference configuration with respect to the angular position of the passive joint. We also demonstrated that the two types of stable giant swing motions could be accomplished by the configuration method.