Darci Luiz Tomasi

Universidade Federal do Paraná, Universidade Tuiuti do Paraná

Papers

Total Citations

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About

Darci Luiz Tomasi is a robotics researcher whose work centers on mobile robot navigation, localization, and control—particularly for differential and omnidirectional platforms. His most impactful contribution, “Rotational odometry calibration for differential robot platforms” (2017, 10 citations), addresses a fundamental challenge in robotics: correcting systematic errors in wheeled robot positioning during navigation. This work provides practical calibration methods that improve odometry accuracy, a critical need for autonomous systems operating in unknown environments. Tomasi also explores cutting-edge bio-inspired navigation in “CBNAV: Costmap Based Approach to Deep Reinforcement Learning Mobile Robot Navigation” (2021, 2 citations), where he integrates cockroach sensor adaptation and insect brain-inspired spatial-temporal learning into a deep reinforcement learning framework. This novel approach enables agents to construct costmaps and navigate unknown spaces without prior maps. Additionally, his work on “Three Wheeled Omnidirectional Mobile Robot - Design and Implementation” (2021, 1 citation) details the complete kinematic modeling and hardware implementation for omnidirectional platforms. Across these contributions, Tomasi bridges classical calibration techniques with modern reinforcement learning, offering practical and innovative solutions for mobile robotics.