Ranulfo Bezerra

Ranulfo Bezerra

Assistant Professor

Tohoku University

Brief Bio

Assistant Professor in the Tohoku Robotic Systems Laboratory (TRSLab) and Tough Cyberphysical AI Research Center (TCPAI)at Tohoku University conducting research on reasoning-aware AI for connected robotic and sensor systems, with the goal of enabling collective intelligence under partial observability. My work focuses on system-level architectures for multi-robot coordination, distributed sensing, and knowledge-driven decision-making, integrating probabilistic modeling, learning-based methods, and semantic representations. I received my Ph.D. degree in Information Sciences from Tohoku University, Japan in 2021. I received my M.Sc. and B.Sc. in computer science from the Federal University of Piaui, Brazil in 2018, and 2016 respectively. My research interests are robot intelligence and robotic perception. A member of RSJ and IEEE.

Interests
  • Intelligent Robotic Systems
  • Internet of Robotic Things
  • Multi-robot Systems
  • Robotic Perception
  • Knowledge Acquisition
Education
  • PhD in Information Sciences, 2021

    Tohoku University

  • Msc in Computer Science, 2018

    Federal University of Piaui

  • BSc in Computer Science, 2016

    Federal University of Piaui

Recent News

Dr. Ranulfo Bezerra Invited to Join the Editorial Board of IEEE IoT Journal

2023-10-02

Two Research Contributions Accepted at Upcoming 21st International Conference on Advanced Robotics (ICAR)

2023-10-02

Two Research Contributions Accepted at Upcoming IEEE Systems, Man, and Cybernetics (SMC) Conference

2023-05-27

Three Research Contributions Accepted at Upcoming IEEE International Conference on Automation Science and Engineering (CASE)

2023-05-25

Projects

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Automation for Transformable Production

Automation for Transformable Production

Multi-robot Task Allocation and Control System on Transformable Production

Disaster Prevention Challenge on World Robot Summit

Disaster Prevention Challenge on World Robot Summit

Discover how a multi-modal robot team excelled in the Disaster Prevention Challenge at the World Robot Summit 2020, enhancing industrial plant inspections.

Knowledge Acquisition from Sparse Mobile Probe Data

Knowledge Acquisition from Sparse Mobile Probe Data

This study provides different techniques to improve the perception for autonomous vehicles by knowledge acquisition.

Optimizing Construction Vehicle Interaction with Machine Learning

Optimizing Construction Vehicle Interaction with Machine Learning

Leveraging Machine Learning for Optimized Interaction Between Autonomous Dump Trucks and Human-Operated Backhoes in Earthmoving Work.

Recent Publications

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(2026). AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics. IEEE Internet of Things Journal.

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(2025). Mapping the Carrier Phase of a Propagating Wave by Wireless Two-Way Interferometry Scanned by a Mobile Robot. IEEE Access.

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(2025). Adaptive Cost-Map-based Path Planning in Partially Unknown Environments with Movable Obstacles. 22st International Conference on Advanced Robotics, ICAR 2025, San Juan, Argentina.

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(2025). Drone Landing Performance in Windy Conditions: Comparing the Vertical and Horizontal Landing Approaches With the EAGLES Port. IEEE Robotics & Automation Magazine.

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(2025). Enhancing Teleoperator Awareness of Gripper-Object Interaction by Modulating Control Button Stiffness. IEEE/SICE International Symposium on System Integration, SII 2025, Munich, Germany, January 21-24, 2025.

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Contact

  • ranulfo (at) tohoku.ac.jp
  • +81 022-795-4871
  • 6-6-01 Aramaki Aza Aoba, Aoba-ku., Sendai, Miyagi 980-8579