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Manohari Goarin

dblp:374/0410 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 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
Motion planning and robot control · 60% Multi-agent systems · 20% Legged, aerial and field robots · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
collision avoidance
0.912025
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range · ICRA 2025
Robotics › Motion planning and robot control
motion planning
0.912025
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range · ICRA 2025
Knowledge, reasoning and agents › Multi-agent systems
multi-robot systems
0.912025
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range · ICRA 2025
Robotics › Legged, aerial and field robots › aerial robots › multi-UAV coordination
quadrotor teams
0.912025
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range · ICRA 2025
Robotics › Motion planning and robot control
robot control
0.912025
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range · ICRA 2025

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

nonlinear model predictive control · 0.9exponential control barrier functions · 0.9control barrier functions · 0.9
YearPublicationVenuePosition
2025 Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range
abstract
Multi-quadrotor systems face significant challenges in decentralized control, particularly with safety and coordination under sensing and communication limitations. State-of-the-art methods leverage Control Barrier Functions (CBFs) to provide safety guarantees but often neglect actuation constraints and limited detection range. To address these gaps, we propose a novel decentralized Nonlinear Model Predictive Control (NMPC) that integrates Exponential CBFs (ECBFs) to enhance safety and optimality in multi-quadrotor systems. We provide both conservative and practical minimum bounds of the range that preserve the safety guarantees of the ECBFs. We validate our approach through extensive simulations with up to 10 quadrotors and 20 obstacles, as well as real-world experiments with 3 quadrotors. Results demonstrate the effectiveness of the proposed framework in realistic settings, highlighting its potential for reliable quadrotor teams operations.
Manohari Goarin, Guanrui Li, Alessandro Saviolo, Giuseppe Loianno
ICRA1