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Pravin Dangol

dblp:264/0164 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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 · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.512021
Generative Design of NU's Husky Carbon, A Morpho-Functional, Legged Robot · ICRA 2021
Robotics › Legged, aerial and field robots
legged robots
0.512021
Generative Design of NU's Husky Carbon, A Morpho-Functional, Legged Robot · ICRA 2021
Mathematical optimization
design optimization
0.112021
Generative Design of NU's Husky Carbon, A Morpho-Functional, Legged Robot · ICRA 2021
Mathematical optimization › design optimization
generative design
0.112021
Generative Design of NU's Husky Carbon, A Morpho-Functional, Legged Robot · ICRA 2021

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

parametric design space search · 1.0evolutionary solver · 1.0
YearPublicationVenuePosition
2021 Generative Design of NU's Husky Carbon, A Morpho-Functional, Legged Robot
abstract
We report the design of a morpho-functional robot called Husky Carbon. Our goal is to integrate two forms of mobility, aerial and quadrupedal legged locomotion, within a single platform. There are prohibitive design restrictions such as tight power budget and payload, which can particularly become important in aerial flights. To address these challenges, we pose a problem called the Mobility Value of Added Mass (MVAM) problem. In the MVAM problem, we attempt to allocate mass in our designs such that the energetic performance is affected the least. To solve the MVAM problem, we adopted a generative design approach using Grasshopper’s evolutionary solver to synthesize a parametric design space for Husky. Then, this space was searched for the morphologies that could yield a minimized Total Cost Of Transport (TCOT) and payload. This approach revealed that a front heavy quadrupedal robot can achieve a lower TCOT while retaining larger margins on allowable added mass to its design. Based on this framework Husky was built and tested as a front heavy robot.
Alireza Ramezani, Pravin Dangol, Eric Sihite, Andrew Lessieur, Peter Kelly
ICRA2