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Eranda Tennakoon

dblp:241/2397 · DBLP profile ↗
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1ranked-venue papers
1as 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 · 1 · 1 first-authorSystems, architecture and hardware · 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.

Artificial intelligence
1 paper
Legged, aerial and field robots · 88% Motion planning and robot control · 12%

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
multi-legged robot
0.412020
Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse · ICRA 2020
Robotics › Legged, aerial and field robots
rough terrain locomotion
0.412020
Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse · ICRA 2020
Robotics › Motion planning and robot control › multi-robot control
follow-the-leader deployment
0.112020
Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse · ICRA 2020
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion
gait planning
0.112020
Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse · ICRA 2020

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

terrain probing · 0.4gait and stance design · 0.4
YearPublicationVenuePosition
2020 Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse
abstract
Multi-legged robots are effective at traversing rough terrain. However, terrains that include collapsible footholds (i.e. regions that can collapse when stepped on) remain a significant challenge, especially since such situations can be extremely difficult to anticipate using only exteroceptive sensing. State-of-the-art methods typically use various stabilisation techniques to regain balance and counter changing footholds. However, these methods are likely to fail if safe footholds are sparse and spread out or if the robot does not respond quickly enough after a foothold collapse. This paper presents a novel method for multi-legged robots to probe and test the terrain for collapses using its legs while walking. The proposed method improves on existing terrain probing approaches, and integrates the probing action into a walking cycle. A follow-the-leader strategy with a suitable gait and stance is presented and implemented on a hexapod robot. The proposed method is experimentally validated, demonstrating the robot can safely traverse terrain containing collapsible footholds.
Eranda Tennakoon, Thierry Peynot, Jonathan Roberts 0001, Navinda Kottege
ICRA1