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Chris Lightcap

dblp:89/618 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 100%
Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction › haptic interface
passive haptic device
0.112011
A novel passive haptic device for simulating a broad range of impedances · ICRA 2011
Haptics and multimodal interaction
tactile display
0.112011
A novel passive haptic device for simulating a broad range of impedances · ICRA 2011
Robotics › Motion planning and robot control
robot calibration
0.112008
Improved Positioning Accuracy of the PA10-6CE Robot with Geometric and Flexibility Calibration · IEEE Trans. Robotics 2008
Robotics › Motion planning and robot control
robot control
0.112008
Improved Positioning Accuracy of the PA10-6CE Robot with Geometric and Flexibility Calibration · IEEE Trans. Robotics 2008
Robotics › Motion planning and robot control › robot kinematics
kinematic modeling
0.012008
Improved Positioning Accuracy of the PA10-6CE Robot with Geometric and Flexibility Calibration · IEEE Trans. Robotics 2008

Methods — techniques the papers use, named apart from their topics

experimental study · 0.1flexible geometric model · 0.1
YearPublicationVenuePosition
2011 A novel passive haptic device for simulating a broad range of impedances
abstract
The strength of a haptic display may be assessed from its range of stable impedances. This paper presents a novel passive haptic display (PARM) capable of simulating a broad range of impedances. It comprises a passive constraint that can simulate both uninhibited motion through free-space and collision into a rigid obstacle. In free-space, the user feels the natural impedance of the end-effector without the added inertia and friction from a transmission or actuator. In a collision with a rigid object, haptic stiffness can be much greater than a traditional impedance haptic display. An experimental study with a 2 DoF planar manipulator illustrates the minimum and maximum impedances that are attainable for this haptic display. The inertial mass and friction in free-space are less than 0.2 kg and 12.3 Ns/m, and the stiffness against a haptic boundary is 465 N/mm. In addition, collision with a haptic object produces a more authentic experience, since there are naturally-occurring high frequency oscillations that result from the physical collision between two objects. This novel haptic display has both the minimum impedance benefits of impedance haptic displays and the maximum impedance benefits of admittance haptic displays.
Chris Lightcap, Hyosig Kang
ICRA1
2008 Improved Positioning Accuracy of the PA10-6CE Robot with Geometric and Flexibility Calibration
abstract
Accurate determination of robot geometric and flexibility parameters permits significant reduction of systematic errors and improved end-effector positioning accuracy. We apply a 30-parameter flexible geometric model to the Mitsubishi PA10-6CE robot and reduce mean/peak positional errors from 1.80/2.45 mm to 0.33/0.71 mm while loaded at 44 N.
Chris Lightcap, Samuel Hamner, Tony Schmitz, Scott A. Banks
IEEE Trans. Robotics1
2007 Dynamic identification of a mitsubishi pa10-6ce robot using motion capture
abstract
This paper discusses a method to identify dynamic parameters in a rigid-link flexible-joint robot model using motion capture. The procedure is divided into two parts. First, the robot dynamics are parameterized in a new form to exclude the stiffness torque, and are solved with a rich set of data. The stiffness torque is then reconstructed from the new parameter set, and a torsional spring model is fit to the measured data. This method offers a better conditioned system of equations for weakly-flexible systems, and more flexibility in the choice of torsional spring model. The two-step identification method is experimentally evaluated on a Mitsubishi PA10-6CE.
Chris Lightcap, Scott A. Banks
IROS1