Electric Field Servoing for robotic manipulation

Wistort, Ryan; Smith, Joshua R.

doi:10.1109/iros.2008.4650721

Cited by 49 publications

(20 citation statements)

References 7 publications

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“…The robotic system was run on Willow Garage's ROS software, 3 and the robotic hand was equipped with electric field sensors [46] which enabled the fingers to detect their proximity to objects. The electric field sensors were used, primarily, to close the fingers on the object simultaneously.…”

Section: A Robotic Equipmentmentioning

confidence: 99%

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps

et al. 2012

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Abstract-We present a novel and simple experimental method called physical human interactive guidance to study humanplanned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction.

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Section: A Robotic Equipmentmentioning

confidence: 99%

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps

et al. 2012

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“…In [34]- [36], three infrared (IR) sensors are mounted on the robot hand as Figure 3, in order to control the robot hand to grasp objects using the information from the sensors readings and the interface component. The IR sensors are used for proximally sensing the object distance and providing a corrective signal for the hand to close in on the object.…”

Section: Sensors Incorporated In Assistive Robotsmentioning

confidence: 99%

Sensors Applied in Healthcare Environments

Menomonie¹

2016

JCC

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“…The hypothesis that pretouch sensing can be useful for manipulation is being explored actively [1] [2][3] [4]. It is viewed as potentially beneficial for manipulation because it provides reliable geometric information in the last centimeter before contact.…”

Section: A Robotic Pretouch Sensingmentioning

confidence: 99%

Seashell effect pretouch sensing for robotic grasping

Jiang

Smith

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-This paper introduces seashell effect pretouch sensing, and demonstrates application of this new sensing modality to robot grasp control, and also to robot grasp planning."Pretouch" refers to sensing modalities that are intermediate in range between tactile sensing and vision. The novel pretouch technique presented in this paper is effective on materials that prior pretouch techniques fail on. Seashell effect pretouch is inspired by the phenomenon of "hearing the sea" when a seashell is held to the ear, and in particular relies on the observation that the "sound of the sea" changes as the distance from the seashell to the head varies. To turn the familiar seashell effect into a sensor for robotic manipulation, a cavity and microphone was built into a robot finger. The sensor detects changes in the spectrum of ambient noise that occur when the finger approaches an object. Environmental noise is amplified most (attenuated least) at the cavity's resonant frequency, which changes as the cavity approaches an object.After introducing the sensing modality and characterizing its performance, the paper describes experiments performed with prototype sensors integrated into a Willow Garage PR2 robot's gripper. We explore two primary applications: (1) reactive grasp control and (2) pretouch-assisted grasp planning. For reactive grasp control, we demonstrate the advantage of pretouch over pressure sensing for sensing objects that are too compliant for the PR2's tactile sensors to detect during the grasp execution. We demonstrate the benefit of seashell effect pretouch over prior pretouch methods by demonstrating that the new method succeeds with materials on which prior methods fail. For pretouch-assisted grasp planning, the pointcloud from the PR2's texture-projection / narrow stereo camera is augmented with additional points collected by recording the position of the robot's end effector when the pretouch sensor detects the object. This method can compensate for points that are missing from the pointcloud either because of depth camera failure or occlusion. For this application, pretouch is advantageous over contact sensing because contact sensing tends to unintentionally displace the object.

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Electric Field Servoing for robotic manipulation

Cited by 49 publications

References 7 publications

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps

Sensors Applied in Healthcare Environments

Seashell effect pretouch sensing for robotic grasping

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