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Eric K. Wang 0002

dblp:246/7733 · also Eric Kevin Wang · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-9124-7951ORCID · verified

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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.

Artificial intelligence
1 paper
Robot navigation and mapping · 54% Legged, aerial and field robots · 46%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
attitude estimation
0.412019
Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019
Robotics › Legged, aerial and field robots › hopping robot
hopping robot control
0.412019
Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019
Robotics › Legged, aerial and field robots
legged robots
0.412019
Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019
Robotics › Robot navigation and mapping
state estimation
0.412019
Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019
Robotics › Robot navigation and mapping › localization
dead reckoning
0.112019
Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019

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

rate gyroscope integration · 0.4encoder-based velocity estimation · 0.4
YearPublicationVenuePosition
2019 Drift-free Roll and Pitch Estimation for High-acceleration Hopping
abstract
We develop a drift-free roll and pitch attitude estimation scheme for monopedal jumping robots. The estimator uses only onboard rate gyroscopes and encoders and does not rely on external sensing or processing. It is capable of recovering from attitude estimate disturbances and, together with onboard velocity estimation, enables fully autonomous stable hopping control. The estimator performs well on a small untethered robot capable of large jumps and extreme stance accelerations. We demonstrate that the robot can follow a rectangular path using onboard dead-reckoning with less than 2 meters of drift over 200 seconds and 300 jumps covering 60 m. We also demonstrate that the robot can operate untethered outdoors under human wireless joystick direction.
Justin K. Yim, Eric K. Wang 0002, Ronald S. Fearing
ICRA2