Ratnangshu Das

dblp:355/5639 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0004-5214-4791ORCID · corroborated

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 2021Theory of computation · 1 · 1 first-author · 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
Multi-agent systems · 25% Reinforcement learning · 25% Motion planning and robot control · 25%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
collision avoidance
0.812024
Safe Multi-Robot Exploration using Symbolic Control · ICRA 2024
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.812024
Safe Multi-Robot Exploration using Symbolic Control · ICRA 2024
Machine learning › Reinforcement learning › exploration
multi-robot exploration
0.812024
Safe Multi-Robot Exploration using Symbolic Control · ICRA 2024
Robotics › Robot navigation and mapping › mobile robot navigation
safe navigation
0.812024
Safe Multi-Robot Exploration using Symbolic Control · ICRA 2024

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

symbolic control · 0.8distance function · 0.8
YearPublicationVenuePosition
2024 Spatiotemporal Tubes for Reach-Avoid-Stay Specifications✱
abstract
This study focuses on synthesizing controllers for unknown dynamics control-affine nonlinear systems, aiming to satisfy reach-avoid-stay (RAS) specifications within prescribed-time. The main objective is to derive a closed-form control law, incorporating a novel notion of spatiotemporal tubes, to guarantee that the system trajectories reach a designated target set while avoiding an unsafe set and adhering to state constraints. The efficacy of this approach is demonstrated through simulation.
Ratnangshu Das, Pushpak Jagtap
HSCC1
2024 Safe Multi-Robot Exploration using Symbolic Control
abstract
Multi-robot exploration is a complex problem that involves multiple robots working in a shared unknown environment. In such scenarios, the safety of the robots is of paramount importance alongside the completion of the exploration task. In this paper, we propose a modular exploration framework that (i) identifies safe frontier targets for multiple robots while taking into account the system dynamics of each robot to ensure collision avoidance with previously unknown obstacles and (ii) ensures that the robots reach their exploration targets while avoiding any obstacles discovered and each other. We employ a scalable approach to generate symbolic controllers for the multi-robot system, utilizing distance functions. We also provide formal guarantees on the safety of the exploration targets and the completion of each exploration run, with the robots avoiding collisions with each other and the obstacles. We test our approach on simulation experiments and a real-world implementation to validate it.
Manas Sashank Juvvi, David Smith Sundarsingh, Ratnangshu Das, Pushpak Jagtap
ICRA3