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D. Carballo

dblp:274/9237 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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
Legged, aerial and field robots · 62% Reinforcement learning · 38%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion
quadruped locomotion
0.412020
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot · ICRA 2020
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
terrain exploration
0.412020
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot · ICRA 2020
Robotics › Legged, aerial and field robots › field robotics
disaster response
0.112020
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot · ICRA 2020
Robotics › Legged, aerial and field robots
field robotics
0.112020
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot · ICRA 2020

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

sensor integration · 0.4foothold evaluation · 0.4filtering · 0.4
YearPublicationVenuePosition
2020 Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot
abstract
Legged robots have been highlighted as promising mobile platforms for disaster response and rescue scenarios because of their rough terrain locomotion capability. In cluttered environments, small robots are desirable as they can maneuver through small gaps, narrow paths, or tunnels. However small robots have their own set of difficulties such as limited space for sensors, limited obstacle clearance, and scaled-down walking speed. In this paper, we extensively address these difficulties via effective sensor integration and exploitation of dynamic locomotion and jumping. We integrate two Intel RealSense sensors into the MIT Mini-Cheetah, a 0.3 m tall, 9 kg quadruped robot. Simple and effective filtering and evaluation algorithms are used for foothold adjustment and obstacle avoidance. We showcase the exploration of highly irregular terrain using dynamic trotting and jumping with the small-scale, fully sensorized Mini-Cheetah quadruped robot.
Donghyun Kim 0002, D. Carballo, Jared Di Carlo, Benjamin Katz, Gerardo Bledt, Bryan Lim, Sangbae Kim
ICRA2