Improving Human Interaction with Robot Teams
Abstract
The goal of the award was the procurement of robotic equipment, UAVs and UGVs that collaborate with themselves and a human operator to execute critical missions. Exploration and navigation in unknown environments is a challenging task for ground rovers. While AGVs are often equipped with a variety of sensors that can detect the surrounding environment, the information that these sensors can provide are limited to the immediate surrounding of the AGVs and do not provide sufficient information about the obstacles and eventual destination of different paths. In these cases, an AGV without additional information would have to proceed down each path, prioritized by the best heuristic, in the hopes that no obstacles or dead end block the path. In time-critical environments such as disaster response and relief, this trial-and-error approach would not suffice. Ground operators need to know which paths are safe and absent of hazards to deliver the required aid to trapped survivors as soon as possible. To enhance the exploration and navigation capabilities of autonomous ground vehicles, we propose FalconEye a heterogeneous mapping solution that combines the sensor input from UAV's to detect the open routes and obstacles that AGV's sensors cannot. Using airborne HD cameras and ground LiDAR sensors, FalconEye creates and operates within a 3Dmap whose range far exceeds the map created by ground-only robotic systems. We designed, implemented and demonstrated in field experiments a system of cooperating UAV and UGV under human supervisory control for search and rescue.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 24, 2019
- Accession Number
- AD1085950
Entities
People
- Katia Sycara
Organizations
- Carnegie Mellon University