Alexandre Rocchi
Papers
1
Total Citations
4
H-Index
1
About
Alexandre Rocchi is a robotics researcher whose work focuses on practical, vision-based solutions for multi-robot autonomous navigation. His key research areas include multi-robot systems, computer vision, and deep learning for robotics. Rocchi's major contribution lies in developing low-cost, accessible approaches to collision avoidance for teams of mobile robots. In his most-cited work, "A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network" (2023), he demonstrated how a single monocular camera, combined with a convolutional neural network for depth estimation, can enable effective coordination and obstacle avoidance without expensive sensors. This approach makes multi-robot systems more feasible for real-world applications. With 4 citations, this paper represents an emerging contribution to the field, highlighting Rocchi's ability to bridge the gap between theoretical deep learning and practical robotic deployment. His work is particularly valuable for researchers and students interested in cost-effective autonomous systems, offering a pathway to scalable multi-robot navigation using widely available hardware.
Research Focus
Key Achievements
Top Papers
- 1