Daniel H. S. Fernandes

Universidade Federal do Rio Grande do Norte

Papers

Total Citations

H-Index

About

Daniel H. S. Fernandes is a robotics researcher specializing in path planning and assistive technologies, with a focus on developing novel algorithms for autonomous navigation in complex environments. His primary contributions lie in advancing the Probabilistic Foam Method (PFM), a technique that uses expanding "bubbles" to map free space and generate collision-free paths for robots and orthotic devices. In his 2018 paper "Safe Path Planning Based on Probabilistic Foam for a Lower Limb Active Orthosis to Overcoming an Obstacle" (9 citations), Fernandes applied PFM to simplify path planning for active orthoses, enabling safer obstacle negotiation for users with mobility impairments. Building on this, his work "Goal-biased probabilistic foam method for robot path planning" (8 citations) introduced an improved, goal-directed variation of PFM that enhances efficiency by biasing bubble propagation toward the target, reducing computational overhead. Though his citation counts are modest, Fernandes’s research bridges theoretical path planning and practical rehabilitation robotics, offering scalable solutions for real-world assistive devices. His work is particularly notable for integrating probabilistic methods with mechanical constraints, laying groundwork for more intuitive and adaptive robotic systems in healthcare and autonomous navigation.