Time-of-flight (ToF) cameras are 3D cameras that hold great promise in many industrial fields; however, differences in the reflective properties (due to material or color) of the target object surface cause systematic errors of several centimeters. Although many methods have been proposed to correct systematic errors occurring in ToF cameras, such methods do not explicitly consider the reflective property of the object surface, and the effectiveness of such corrections have been confirmed only for a limited number of materials. To address these systematic errors, we propose a new depth correction technique that accounts for the reflective properties of the object by using the idea that the measured depth obtained with multiple irradiation pulse widths depends on the reflective property. Experiments using objects of various materials demonstrated the effectiveness of the proposed method.

A practical robotic bin-picking system requires a high grasp success rate for various objects. Also, the system must be capable of coping with various constraints and their changes flexibly. To resolve these issues, this study proposes a novel deep learning-based method that exploits a simulator to generate desired grasping actions. The features of this method are as follows: (1) Grasping conditions for any object can be flexibly customizable in the simulated environment to improve the real-world grasping actions. (2) Sensor input (RGB image) is directly regressed to grasping actions by using convolutional processing. Owing to these features, the system using the proposed method can grasp objects with geometric variations, semi-transparent objects, and objects with a biased center of gravity. Experimental results on a real robot system show that the proposed method exhibits a high grasp success rate for four types of objects with different physical and geometric properties as well as additional constraints of grasping condition.

Autonomous decentralized control is a key concept for understanding the mechanism underlying the adaptive and versatile behavior of animals. Although the design methodology of decentralized control based on a dynamical system approach that can impart adaptability by using coupled oscillators has been proposed in previous studies, it cannot reproduce the versatility of animal behaviors comprehensively. Therefore, our objective is to understand behavioral versatility from the perspective of well-coordinated rhythmic and non-rhythmic movements. To this end, we focus on ophiuroids as simple “best” model living organisms that exhibit spontaneous role assignment of rhythmic and non-rhythmic arm movements, and we model such arm movements by using an active rotator model that can describe both oscillatory and excitatory properties. Simulation results show that the spontaneous role assignment of arm movements is successfully realized by using the proposed model, and the simulated locomotion is qualitatively equivalent to the locomotion of real ophiuroids. This fact can potentially facilitate a better understanding of the control mechanism responsible for behavioral versatility in animals.

This article presents a new method for non-rigid surface registration between a surface model and a surface of an internal organ in a given 3D medical image. The surface is represented with a set of feature points, of which locations are represented by a graphical model. For constructing the representation, a set of corresponding points is distributed on each of training surfaces based on an entropy-based particle system. From these corresponding points, we estimate probability densities of the location of each feature point, the conditional probability distribution of the local image pattern around each feature point, and the probability distributions of relative positions between two neighboring feature points. When a new image is given, these densities are used for estimating the location of each feature point by means of a non-parametric belief propagation. The proposed method can estimate not only the locations of the feature points but also their conditional marginal distributions in a given image. Some experimental results obtained from real X-CT images are presented to show its performance.

Despite its appealing concept, a systematic way of designing autonomous decentralized control system is still lacking. In order to alleviate this, we have so far proposed a design scheme for local sensory feedback control leading to adaptive behaviors from the entire system, based on a so-called discrepancy function that extracts the discrepancies between body (i.e., mechanical system), brain (i.e., control system) and environments. This paper intensively investigates the validity of this design scheme under the real world constraints by taking a two-dimensional serpentine robot exhibiting undulatory slithering locomotion as a practical example. The experimental results show that the robot exhibits adaptive behavior against environmental changes as well as the robustness against malfunctions of body segments due to the local sensory feedback control. The results obtained are expected to shed a new light on methodology for autonomous decentralized control system.

Despite its appealing concept, a systematic way of designing autonomous decentralized control system is still lacking. In order to alleviate this, we have so far proposed a design scheme for local sensory feedback control based on so-called discrepancy function that extracts the discrepancies between body (i.e., mechanical system), brain (i.e., control system) and environments. This paper intensively investigates the validity of this design scheme by taking a two-dimensional serpentine robot exhibiting undulatory slithering locomotion as a practical example. Preliminary simulation results derived strongly indicate that our design methodology allows us to endow the robot with highly adaptability and fault tolerance. These results obtained are expected to shed a new light on design methodology for autonomous decentralized control system. Furthermore, together with a validity verification of the simulation results, this paper introduces a real two-dimensional serpentine robot that is currently under development.

When ultrafast laser pulses are tightly focused inside a transparent medium, various nonlinear optical phenomena readily occur at the focal point. These phenomena at the focal point can be utilized for nonlinear-optical microscopy, nanosurgery inside biological samples, device fabrication inside transparent materials, and welding of transparent materials. We review these new techniques from a unified viewpoint.

This study is intended to deal with the interplay between control and mechanical systems, and to discuss the "brain-body interaction as it should be" particularly from the viewpoint of learning. To this end, we have employed a decentralized control of a two-dimensional serpentine robot consisting of several identical body segments as a practical example. The preliminary simulation results derived indicate that the convergence of decentralized learning of locomotion control can be significantly improved even with an extremely simple learning algorithm, i.e., a gradient method, by introducing biarticular muscles compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be off loaded from the brain into its body, which allows robots to emerge various interesting functionalities.

The basics of three-dimensional (3-D) and spectral imaging techniques that are based on the detection of coherence functions and other related techniques are reviewed. The principle of the 3-D source retrieval is based on understanding the propagation law of optical random field through the free space. The 3-D and spectral information are retrieved from the cross-spectral density function of optical random field or numerical calculation of the inverse propagation of the cross-spectral density. We will first introduce the coherence-based spectral tomography techniques with low-coherence light sources. These techniques limit their scheme of coherence detection only along the optical axis and some of them achieve simultaneously the high resolution and high speed of detection taking advantage of an imaging lens. We then provided explanations of the principle of 3-D source retrieval that is based on the propagation law of optical random field through the free space along with the introduction of the numerical holography and computed tomography techniques. We will lastly show 3-D spectral imaging schemes with the concurrent laser-scanning cross-sectioning techniques: one is the confocal laser scanning microscopy and the other is the two-photon laser-scanning fluorescence microscopy.