Preliminary Study on the Crawler Unit of a Novel Self-Reconfigurable Hybrid Platform for Inspection

Sergio Leggieri, Carlo Canali, Ferdinando Cannella, Jinoh Lee, Darwin G. Caldwell
2021 2021 20th International Conference on Advanced Robotics (ICAR)  
Inspections of either industrial and civil structures are necessary to prevent damages and loss of human life. Although robotic inspection is gaining momentum, most of the operations are still performed by human workers. Many are the factors that slow down the spread of inspection robots, in particular, the lack of versatility as well as the low reliability of these devices constitute a huge limitation. In this work, we propose a design of a hybrid platform in the context of industrial
more » ... n tasks. The aim is to address versatility issues exploiting modularity and self-reconfigurability. The final platform will consist of three main components: a main mobile base and two vehicles. All these systems would operate independently accomplishing specific inspection tasks. However, docking interfaces on each device will allow the systems to reconfigure into different robots extending the application range of each unit. The vehicles will work mainly in constrained environments and narrow spaces. The mobile base will monitor wide areas, carrying around the vehicles and deploying them near the inspection target. For dealing with challenging conditions, the two crawlers will dock together, reconfiguring into a snake-like robot. Docking to the main base, the two vehicle would act also as robotic arms, providing manipulation abilities to the system, thus allowing to perform maintenance operations as well. Still, the project is at an early stage of development. Revisions or adjustments on the prototype may follow the evaluations on the crawler performance. *This research was partly supported by ITECH R&D program of MOTIE/KEIT (No. 20014485 Development of small-size, high-precision and 500g-payload capable collaborative robot technology.)
doi:10.1109/icar53236.2021.9659426 fatcat:5yy7b7kmcnhuvnjagtx6hrjd6i