Development of sensitive skin for a 3D robot arm operating in an uncertain environment

Cheung, E.; Lumelsky, V.

doi:10.1109/robot.1989.100121

Cited by 40 publications

(17 citation statements)

References 5 publications

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“…The sensors' output voltage is proportional to the amount of reflected light. This analog signal is used for extracting the proximity data, and is converted to local normal information by the step planning procedure described in [1]. The procedure calculates the local normals at the contact point to the obstacle in the C-space.…”

Section: Sensor Skin and Robot Armmentioning

confidence: 99%

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Real-time path planning procedure for a whole-sensitive robot arm manipulator

Cheung

Lumelsky²

1992

Robotica

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SUMMARYWe consider the problem of sensor-based motion planning for a three-dimensional robot arm manipulator operating among unknown obstacles. When every point of the robot body is subject to potential collision. The corresponding planning system must include these four basic components: sensor hardware; real-time signal/sensory data processing hardware/software; a local step planning subsystem that works at the basic sample rate of the arm; and finally, a subsystem for global planning. The arm sensor system developed at Yale University presents a proximity sensitive skin that covers the whole body of the arm and consists of an array of discrete active infrared sensors that detect obstacles by processing reflected light. The sensor data then undergoes low level processing via a step planning procedure, which converts sensor information into local normals at the contact points in the configuration space of the robot. This paper presents preliminary results on the fourth component, a real-time algorithm that realizes the upper, global level of planning. Based on the current collection of local normals, the algorithm generates preferable directions of motion around obstacles, so as to guarantee reaching the target position if it is reachable. Experimental results from testing the developed system are also discussed.

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Section: Sensor Skin and Robot Armmentioning

confidence: 99%

“…Obstacles appearing from any direction can then be detected, and appropriate action taken to avoid them. Such a sensor system based on the infrared light sensitive skin, together with local sensor interpretation algorithms, has been developed at Yale University [1]. The last element to be added to complete the motion planning system is an…”

Section: Introductionmentioning

confidence: 99%

Real-time path planning procedure for a whole-sensitive robot arm manipulator

Cheung

Lumelsky²

1992

Robotica

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“…There has been much research on haptic devices designed to develop tactile sensors for robot hands and fingertips [1,4]. These sensors have mainly been designed to detect contact between a robot's hands and the target objects to be grasped or manipulated.…”

Section: Introductionmentioning

confidence: 99%

Recognizing human touching behaviors using a haptic interface for a pet-robot

Naya

Yamato²,

Shinozawa³

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028)

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This paper presents the preliminary results of classifying human touching behaviors using a haptic interface for a pet-like robot. The haptic interface uses gridded pressure-sensitive conductive ink sheets. Features of the measured pressure data are determined for classification in terms of 1) absolute values, 2) spatial distributions and 3) the temporal differences in measured pressure patterns. Touching behaviors include "slap," "pat," "stroke" and so forth. The experimental results show that a reliable classification of these touching patterns can be accomplished by using the sensor sheet and pressure features. The results of classification can be used as reward signals for reinforcement learning in controlling the behaviors of a pet-like robot that interacts with humans.

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“…One of the first attempts to produce artificial sensing skin for robotic applications was introduced by Lumelsky et al [3,4] Their modular skin device used multiple infrared sensors to detect proximity and contact of an object. Other researchers have proposed exploiting the change in capacitance between two soft planes [5,6] or the variation in the resistance of a piezoresistive material [7][8][9] as means of localizing and measuring applied stresses [10].…”

Section: Introductionmentioning

confidence: 99%

Soft Tactile Skin Using an Embedded Ionic Liquid and Tomographic Imaging

Chossat

Shin

Park

et al. 2015

Journal of Mechanisms and Robotics

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Whole-body-contact sensing will be crucial in the quest to make robots capable of safe interaction with humans. This paper describes a novel design and a fabrication method of artificial tactile sensing skin for robots. The manufacturing method described in this paper allows easy filling of a complex microchannel network with a liquid conductor (e.g., room temperature ionic liquid (RTIL)). The proposed sensing skin can detect the magnitude and location of surface contacts using electrical impedance tomography (EIT), an imaging technique mostly used in the medical field and examined recently in conjunction with sensors based on a piezoresistive polymer sheet for robotic applications. Unlike piezoresistive polymers, our IL-filled artificial skin changes its impedance in a more predictable manner, since the measured value is determined by a simple function of the microchannel geometry only, rather than complex physical phenomena. As a proof of concept, we demonstrate that our EIT artificial skin can detect surface contacts and graphically show their magnitudes and locations.

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Development of sensitive skin for a 3D robot arm operating in an uncertain environment

Cited by 40 publications

References 5 publications

Real-time path planning procedure for a whole-sensitive robot arm manipulator

Real-time path planning procedure for a whole-sensitive robot arm manipulator

Recognizing human touching behaviors using a haptic interface for a pet-robot

Soft Tactile Skin Using an Embedded Ionic Liquid and Tomographic Imaging

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