Daniele Caldana
Instituto Superior de Contabilidade e Administracao do Porto
Papers
1
Total Citations
2
H-Index
1
About
Daniele Caldana is a researcher at the forefront of industrial automation and artificial intelligence, with a focused expertise in pallet detection and location using commercial off-the-shelf (COTS) sensors. His work bridges the gap between practical logistics challenges and cutting-edge AI applications, addressing critical needs in warehouse automation and supply chain efficiency. Caldana’s most-cited paper, "Comparison of Pallet Detection and Location Using COTS Sensors and AI Based Applications" (2024), has already garnered 2 citations, signaling early impact in a rapidly evolving field. This study systematically evaluates sensor technologies—such as cameras and LiDAR—combined with AI algorithms to optimize pallet recognition, a cornerstone task for autonomous forklifts and robotic material handling. By comparing cost-effective sensors against advanced AI models, Caldana provides actionable insights for industry practitioners seeking scalable solutions. His work contributes to reducing operational costs and improving accuracy in logistics, with potential applications in smart factories and e-commerce fulfillment centers. As a researcher, Caldana demonstrates a commitment to translating theoretical AI advances into real-world industrial systems, making his contributions valuable for both academics and engineers aiming to enhance automation in complex environments.
Research Focus
Key Achievements
Top Papers
- 1