Fault tolerant approach for biosignal-based robot control

This paper proposes a fault tolerant framework for biosignal-based robot control with multiple sensor electrodes. In this approach, to cope with sensor faults, a reliable joint torque estimation model is selected from a group of estimation models based on sensor failure classifiers. The correlation among the electromyography (EMG) signal streams is used as input feature vectors for fault detection. To validate our proposed method, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow-joint torque estimation experiments with five participants. When one EMG sensor electrode experiences one of the problems, the experimental results show that the joint torque can be estimated with significantly fewer errors using our proposed approach than a joint torque estimation method without sensor fault detection or than a method with a conventional sensor fault detection algorithm. Furthermore, we controlled a mannequin-arm-attached one-DOF exoskeleton based on the estimated torque profiles by generating movements with the estimated torque derived from the selected model.     

Laser range scanner based on self-calibration techniques using coplanarities and metric constraints

In this paper, we propose a novel method to achieve both dense 3D reconstruction of the scene and estimation of the camera intrinsic parameters by using coplanarities and other constraints (e.g., orthogonalities or parallelisms) derived from relations between planes in the scene and reflected curves of line lasers captured by a single camera. In our study, we categorize coplanarities in the scene into two types: implicit coplanarities, which can be observed as reflected curves of line lasers, and explicit coplanarities, which are, for example, observed as walls of a building. By using both types of coplanarities, we can construct simultaneous equations and can solve them up to four degrees of freedom. To upgrade the solution to the Euclidean space and estimate the camera intrinsic parameters, we can use metric constraints such as orthogonalities of the planes. Such metric constraints are given by, for example, observing the corners of rectangular boxes in the scene, or using special laser projecting device composed of two line lasers whose laser planes are configured to be perpendicular.     

A biped static balance control and torque pattern learning under unknown periodic external forces

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.