Robotic Monitoring of Habitats: The Natural Intelligence Approach

Angelini, Franco; Angelini, Pierangela; Angiolini, Claudia; Bagella, Simonetta; Bonomo, Fabio; Caccianiga, Marco; Santina, Cosimo Della; Gigante, Daniela; Hutter, Marco; Nanayakkara, Thrishantha; Remagnino, Paolo; Torricelli, Diego; Garabini, Manolo

Abstract

In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes.

Citation

Angelini, F., Angelini, P., Angiolini, C., Bagella, S., Bonomo, F., Caccianiga, M., …Garabini, M. (2023). Robotic Monitoring of Habitats: The Natural Intelligence Approach. IEEE Access, 11, 72575-72591. https://doi.org/10.1109/access.2023.3294276

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2023
• Online Publication Date: Jul 11, 2023
• Publication Date: Jul 11, 2023
• Deposit Date: Jul 31, 2023
• Publicly Available Date: Jul 31, 2023
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Pages: 72575-72591
• DOI: https://doi.org/10.1109/access.2023.3294276
• Keywords: General Engineering; General Materials Science; General Computer Science; Electrical and Electronic Engineering
• Public URL: https://durham-repository.worktribe.com/output/1708428