Keisuke Koyama

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.

Net-Structure Proximity Sensor: High-Speed and Free-Form Sensor With Analog Computing Circuit

This paper proposes a proximity sensing system, which has advantages of wide sensing area and rapid response. For the rapid and safe behavior of robots, high-speed detection of nearby and noncontact objects is important because of shorter time-to-contact. Here, we propose net-structure proximity sensor (NSPS), which covers large sensing area and fulfills 1-ms response time. NSPS is an array of infrared reflective proximity sensor elements integrated by a resistor network circuitry. Executing analog computation on the electrical circuitry, the sensing system outputs a few of meaningful signals, from the reaction distribution of all the elements. The signals mean the center position and approximate distance to the object. This sensor requires only six external wires regardless of the number of detecting elements. In this paper, we first show that various sizes of NSPS are easily configured by only using standard electronic parts. Next, we prototype NSPS with 25 elements in 5 × 5 matrix, and verify the output characteristics by experiments. At last, we discuss the availability of NSPS for robot hand systems and human-machine interface systems.

Grasping strategy for moving object using Net-Structure Proximity Sensor and vision sensor

This study presents a robot-hand-arm system with high robustness and responsiveness by using a “Net-Structure Proximity Sensor.” The sensor, which we have developed and specially designed for a robot hand, directly detects an object being to be grasped and outputs analog voltage signals according to the position/posture error between the robot hand and the object. It has been confirmed that the robot hand is able to quickly adjust to and grasp an unknown object by applying a feed-back control method based on the sensor signals. This paper focuses on the integration of the proximity-based feedback control to a commonly-used vision-based control. These sensors work in complementary manner: a vision sensor is available for planning an approaching path of a robot hand by detecting large area, and a Net-Structure Proximity Sensor enables the robot hand to adjust the approaching error before grasping and to improve the certainty of the grasping. Two objective velocities are derived independently by the sensors. By adding the velocities with considering the reliability of the sensor information, the robot hand becomes to be able to perform approaching and adjustment to the target object simultaneously. Experimental results showed that the robot hand grasped a moving object with high success rate even in conditions where it was difficult to predict the trajectory of the object accurately.

Grasping control based on time-to-contact method for a robot hand equipped with proximity sensors on fingertips

Quick motion and soft touch control are important for autonomous grasping. To perform a grasping action, a hand must adjust its fingertips to match the object shape. It is also necessary to reduce the fingertip velocity on contact with the object. We propose a method of fingertip velocity control for fast approach and slow contact using proximity sensors installed on fingertips. The proposed control reduces the fingertip velocity at contact, decreasing the change of impulse force and thus making it appropriate for grasping soft or fragile objects. The proposed control uses time-to-contact (TTC), which is converted from sensor output. It is known that many animals, including humans, use TTC for collision avoidance. TTC represents the remaining time until collision considering the rate change of a control variable. We carried out grasping tests on various common objects using TTC. Experimental results show that the proposed control realizes fingertip alignment perpendicular to unknown shapes and surface objects and lowers the velocity at contact.

Integrated control of a multi-fingered hand and arm using proximity sensors on the fingertips

In this study, we propose integrated control of a robotic hand and arm using only proximity sensing from the fingertips. An integrated control scheme for the fingers and for the arm enables quick control of the position and posture of the arm by placing the fingertips adjacent to the surface of an object to be grasped. The arm control scheme enables adjustments based on errors in hand position and posture that would be impossible to achieve by finger motions alone, thus allowing the fingers to grasp an object in a laterally symmetric grasp. This can prevent grasp failures such as a finger pushing the object out of the hand or knocking the object over. Proposed control of the arm and hand allowed correction of position errors on the order of several centimeters. For example, an object on a workbench that is in an uncertain positional relation with the robot, with an inexpensive optical sensor such as a Kinect, which only provides coarse image data, would be sufficient for grasping an object.