Anselmo Rafael Cuckla

About

Anselmo Rafael Cuckla is a robotics researcher whose work sits at the intersection of autonomous navigation, computer vision, and reinforcement learning. His primary research focuses on developing intelligent systems for mobile robots, with a particular emphasis on mapless navigation—enabling robots to move through unknown environments without relying on pre-built maps. In his most cited work, "Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots" (2023, 4 citations), Cuckla compares Deep Q-Network (DQN) and Double Deep Q-Network algorithms, demonstrating how deep reinforcement learning can enhance a robot's ability to navigate using only low-dimensional sensor data. His earlier project, "Project of a Sentinel Robot Controlled with a Tracking Algorithm" (2022, 2 citations), showcases his practical engineering skills, integrating Google's MediaPipe algorithm for real-time person tracking with a two-degree-of-freedom camera system. This work highlights his ability to bridge the gap between advanced AI techniques and tangible robotic hardware. With a growing citation footprint, Cuckla is establishing himself as a promising voice in the field of intelligent robotics, contributing to the next generation of autonomous systems that are both smarter and more adaptable.

Research Focus

Key Achievements

Top Papers

1. 1
   Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

   4 citations · 2023
2. 2
   Project of a Sentinel Robot Controlled with a Tracking Algorithm

   2 citations · 2022
3. 3
   Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

   2 citations · 2023

Key Collaborators

Contact & Links