Ajay Giri Prakash Kottapalli

dblp:26/11184 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-3868-7069ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 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
Robot manipulation · 62% Motion planning and robot control · 38%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.612022
Source-Seeking Control of Unicycle Robots With 3-D-Printed Flexible Piezoresistive Sensors · IEEE Trans. Robotics 2022
Robotics › Motion planning and robot control › robot control › optimization-based control
gradient-based control
0.212022
Source-Seeking Control of Unicycle Robots With 3-D-Printed Flexible Piezoresistive Sensors · IEEE Trans. Robotics 2022
Robotics › Motion planning and robot control
mobile robot control
0.212022
Source-Seeking Control of Unicycle Robots With 3-D-Printed Flexible Piezoresistive Sensors · IEEE Trans. Robotics 2022

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

projected gradient ascent · 0.6extremum seeking control · 0.6
YearPublicationVenuePosition
2022 Source-Seeking Control of Unicycle Robots With 3-D-Printed Flexible Piezoresistive Sensors
abstract
In this article, we present the design and experimental validation of source-seeking control algorithms for a unicycle mobile robot that is equipped with novel 3-D-printed flexible graphene-based piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source-seeking problem. In the case of partial sensor failure, we propose a combination of extremum-seeking control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms, and experimental validations are presented to demonstrate the efficacy of the proposed methods.
Bayu Jayawardhana, Amar Kamat, Ajay Giri Prakash Kottapalli
IEEE Trans. Robotics4