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Reinaldo Figueroa

dblp:330/3717 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

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
Motion planning and robot control · 87% Legged, aerial and field robots · 13%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › trajectory planning
decentralized trajectory planning
0.712023
Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay · ICRA 2023
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning
0.712023
Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay · ICRA 2023
Robotics › Legged, aerial and field robots
aerial robots
0.212023
Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay · ICRA 2023

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

trajectory optimization · 0.7delay check · 0.7
YearPublicationVenuePosition
2023 Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay
abstract
Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly realistic in real-world experiments. This paper presents Robust MADER (RMADER), a decentralized and asynchronous multiagent trajectory planner that can handle communication delays among agents. By broadcasting both the newly optimized trajectory and the committed trajectory, and by performing a delay check step, RMADER is able to guarantee safety even under communication delay. RMADER was validated through extensive simulation and hardware flight experiments and achieved a 100% success rate of collision-free trajectory generation, outperforming state-of-the-art approaches.
Kota Kondo, Jesus Tordesillas, Reinaldo Figueroa, Juan Rached, Joseph Merkel, Parker C. Lusk, Jonathan P. How
ICRA3