Paolo Lino

About

Paolo Lino’s research bridges intelligent control, autonomous systems, and precision agriculture, with a focus on modeling and simulation of hybrid systems. His early work pioneered the integration of fuzzy logic with Discrete Event System Specification (DEVS), leading to the Intelligent-DEVS (I-DEVS) framework—a robust environment for modeling, simulating, and controlling autonomous agents. This foundational contribution, detailed in his 2004 paper (7 citations) and the 2003 V-Lab® platform (6 citations), established a distributed, intelligent discrete-event architecture that remains influential in multi-agent simulation. More recently, Lino has advanced robotic control and agricultural automation. His 2021 study on fractional-order PIν controllers for 5DOF manipulators (2 citations) demonstrates novel independent-joint control strategies with improved precision. In 2022, he developed adaptive UAV trajectory generation for detecting Xylella Fastidiosa disease in olive trees (2 citations), integrating ROS, Gazebo, and MoveIt to create a realistic simulation environment for autonomous inspection. This work highlights his ability to translate theoretical control methods into practical, high-impact solutions for real-world challenges. With a career spanning foundational simulation theory to applied robotics and agricultural technology, Lino’s research continues to shape intelligent autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   An Intelligent Discrete Event Approach to Modeling, Simulation and Control of Autonomous Agents

   7 citations · 2004
2. 2
   V-Lab^®– A Distributed Intelligent Discrete-Event Environment for Autonomous Agents Simulationi

   6 citations · 2003
3. 3
   Independent-joint Control of 5DOF Robotic Manipulators by Fractional-Order PI^ν Controllers

   2 citations · 2021
4. 4
   UAV Adaptive Trajectory for Detection of Xylella Fastidiosa Disease in Olive Trees

   2 citations · 2022

Key Collaborators

Contact & Links