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About

Fernando Quevedo is a robotics researcher whose work focuses on path planning, trajectory optimization, and soft robotics. His major contributions lie in advancing the Fast Marching Square (FM²) method for geometrically constrained and multi-agent systems, including robotic grasping and UAV swarm coordination. His 2022 paper on geometrically constrained path planning for robotic grasping (17 citations) and his 2023 work on 4D trajectory planning for UAV teams (11 citations) demonstrate his impact in developing efficient, real-world deployable algorithms. Quevedo has also made notable strides in soft robotics, particularly through his work on model identification of a soft robotic neck (9 and 5 citations), addressing the challenge of controlling nonlinear, bio-inspired actuators. His 2023 paper combining Gaussian processes with FM² for informative path planning (7 citations) highlights his integration of machine learning with classical planning techniques. Quevedo’s research is characterized by its practical focus on autonomous systems, from ground robots to aerial swarms, and his methods have been cited for their effectiveness in complex, constrained environments. His work continues to influence the fields of robotic manipulation, exploration, and soft robotics.