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Moisés Bonilla Estrada

dblp:02/8093 · also Moisés Bonilla E., Moisés E. Bonilla · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0003-3954-7380ORCID · verified

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

Artificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

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 · 67% Robot navigation and mapping · 33%
Computer networks
1 paper
Physical-layer communications · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot navigation › navigation planning
communication-aware trajectory planning
0.412020
Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge · IEEE Trans. Robotics 2020
Robotics › Motion planning and robot control › trajectory optimization
minimum-energy trajectory
0.412020
Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge · IEEE Trans. Robotics 2020
Robotics › Motion planning and robot control
trajectory planning
0.412020
Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge · IEEE Trans. Robotics 2020
Physical-layer communications › channel modeling › propagation channel modeling
wireless channel modeling
0.112020
Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge · IEEE Trans. Robotics 2020

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

trajectory optimization · 0.9
YearPublicationVenuePosition
2020 Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge
abstract
Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. In this article, we consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method.
Daniel Bonilla Licea, Moisés Bonilla Estrada, Mounir Ghogho, Samson Lasaulce, Vineeth S. Varma
IEEE Trans. Robotics2
2017 Non destructive detection of Zea mays' critical periods: A parameter identification based approach
Moisés Bonilla Estrada, Juan C. Martinez-Garcia, C. Antonio, R. Arteaga
Neurocomputing1