Yosuke Suzuki

Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude

In this paper, we focus on unknown parameters such as the position and attitude of the object, and describe a short-range, high-speed and noncontact sensing method for obtaining the position and attitude of the object using IR Net-Structure Proximity Sensor (“IR-NSPS”) which complements the dead region of sensory information between visual and tactile sensing. To be more precise, we propose two effective control methods which are pre-shaping and object positioning using IR-NSPS for robust grasping by adjusting the gripper configuration in response to attitude error of up to ±45 deg and the position error of up to ±80 mm of the unknown object. The methods therefore can significantly increase the speed and effectiveness of grasping objects without requiring a specific approach that depends on a vision sensor. Furthermore, to demonstrate the advantages of pre-shaping and object positioning, object grasping experiments were performed using these two operations to grasp objects placed randomly on a tabletop.

Pre-shaping for various objects by the robot hand equipped with resistor network structure proximity sensors

In this paper, we demonstrate a preliminary motion before grasping by a robot hand, for adjusting the object-fingertip distance and 2-axis postures simultaneously, using a Resistor Network Structure Proximity sensor (RNSP sensor). Through this motion (called “pre-shaping”) and the grasping of an object, the surface of each fingertip is brought into contact with the object surface so that in the next stage grasping can be undertaken. In the next stage, a force can be applied from the fingertips onto the object surface directly. The pre-shaping enhances the reliability of the feedback control for the after-contact tactile sensors. To realize the pre-shaping, we use fingertips equipped with RNSP sensors, which can detect the distance between the fingertip and the object, to determine the relative position between fingertips and an object. The RNSP sensor has a fast response (<;1 [ms]) and simple connectivity (only 6 wires), and can be mounted easily. Additionally, a characteristics of the RNSP sensor output can be designed by the arrangement of the sensor elements. To perform the pre-shaping by simple sensor feedback control based on the configuration between the fingertip and object, we designed the RNSP sensor so that it had the appropriate characteristics for the pre-shaping.

Reconfigurable group robots adaptively transforming a mechanical structure - numerical expression of criteria for structural transformation and automatic motion planning method -

This study describes a group robot system CHOBIE II. The feature of the CHOBIE II is a function to form a reconfigurable structure. In the previous paper, we introduced a control method of the robots and realized transformation of the structure. In this paper, we focus on a motion planning method to obtain control algorithms. For this purpose, we introduce a numerical criterion for generating transformations. The criterion is expressed with matrix form of 32 parameters. This paper demonstrates the criteria generate various motions of the CHOBIE II, and proposes a new method to obtain the appropriate parameters for objective motions.

Hemispherical net-structure proximity sensor detecting azimuth and elevation for guide dog robot

We have developed a net-structure proximity sensor that detects the azimuth and elevation to a nearby object. This information can be used by robots to avoid obstacles or to respond to human behavior. We propose detection principles where the azimuth is detected by arranging two one-dimensional net-structure proximity sensors along orthogonal axes, and the elevation is detected by arranging two one-dimensional net-structure proximity sensors in a stacked ring. We also experimentally demonstrate the feasibility of these detection principles. The experimental result shows the sensor can detect azimuth at all peripheral angles and elevation from side to top up.

Reconfigurable group robots adaptively transforming a mechanical structure - Extended criteria for load-adaptive transformations -

This study deals with a control method of a modular robotic system ldquoCHOBIE II.rdquo Our purpose is to realize a function that CHOBIE II forms a self-reconfigurable structure adaptively to mechanical environments. In the previous paper, we introduced a method to express criteria for generating transformations. A criterion is regulated by 32 parameters corresponding to 32 kinds of local shapes on contour of the structure. According to the criterion, CHOBIE II can dissolve undesirable local shapes and transform to goal configurations. In this paper, we propose a method to realize load-adaptive motion extending the criterion. Specifically, we add two new criteria to induce desirable local shapes and to change priorities of motions depending on a load condition. We show the availability of the method by simulation results that the modular robots construct a cantilever structure avoiding overstressed states.

Reconfigurable Modular Robots Adaptively Transforming a Mechanical Structure (Numerical Expression of Transformation Criteria of “CHOBIE II” and Motion Experiments)

This study describes a group robotic system “CHOBIE II.” The feature of CHOBIE II is a function to form a self-reconfigurable structure. We focus on a motion planning method to obtain control algorithms. For this purpose, we introduce a numerical criterion for generating transformations. The criterion is expressed with a matrix form of 32 parameters. The criteria facilitate implementation of various motions of CHOBIE II, not requiring a high information processing unit. We demonstrate two types of transformations of CHOBIE II with the numerical expression. It is confirmed that the proposed method has advantages of simplification and downsizing of control algorithms of the modules.

Three-dimensional object recognition using two-dimensional complex amplitude including three-dimensional shape information

The fringe pattern correlator (FPC) can recognize 3D objects using a height transformed complex amplitude as 3D information. The height transformed complex amplitude is a two-dimensional (2D) complex amplitude whose phase factor includes actual height information of 3D objects. The FPC can perform effective 3D correlation using 2D correlation with the height transformed complex amplitude. In this study, we describe the effect of the amplitude factor of the height transformed complex amplitude in the FPC. The amplitude factor represents contrast information of the projected grating on the object, which corresponds to the reflectance property depending on 3D shape. We classify the amplitude factor by the 3D shape (flat surface, slope, edge, large step) and other factor (illumination condition, shadow, random pattern). We present the recognition characteristics with respect to the amplitude factor.

Surface Texture of Deformable Robotic Fingertips for a Stable Grasp Under Both Dry and Wet Conditions

This letter investigates the effect of the surface texture of soft deformable fingertips on the maximum resistible force under dry and wet conditions, and proposes a new hybrid structure that provides a stable grasp under both conditions. One definition of stable grasp is the capability of balancing a large external force or moment while grasping. For soft fingertips, both the friction and surface deformation contribute to the stability. Therefore, we investigate the maximum resistible force, which is defined as the maximum tangential force at which the fingertip can maintain contact when applying and increasing the tangential/shear force. We investigate the slit textures with primitive patterns and demonstrate that the nonpattern performs the best under dry conditions, whereas the horizontal slit pattern performs the best under wet (oily) conditions. Based on this, a concentric hybrid texture of the two patterns is proposed, and its effectiveness is verified by a grasping test.

Self-Reconfigurable Modular Robots Adaptively Transforming a Mechanical Structure: Algorithm for Adaptive Transformation to Load Condition

Self-reconfigurable modular robots are composed of modules which are able to autonomously change the way they are connected. An appropriate control algorithm enables the modular robots to change their shape in order to adapt to their immediate environment. In this paper, we propose an algorithm for adaptive transformation to load condition of the modular robots. The algorithm is based on a simple idea that modules have tendency to gather around stress-concentrated parts and reinforce the parts. As a result of the self-reconfiguration rule, the modular robots form an appropriate structure to stand for the load condition. Applying the algorithm to our modular robot named “CHOBIE II,” we show by computer simulation that the modules are able to construct a cantilever structure with avoiding overstressed states.