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Title: Deep Learning–based Reassembling of an Aerial & Legged Marsupial Robotic System–of–Systems
In this work we address the System-of-Systems reassembling operation of a marsupial team comprising a hybrid Unmanned Aerial Vehicle and a Legged Locomotion robot, relying solely on vision-based systems and assisted by Deep Learning. The target application domain is that of large-scale field surveying operations under the presence of wireless communication disruptions. While most real-world field deployments of multi-robot systems assume some degree of wireless communication to coordinate key tasks such as multi-agent rendezvous, a desirable feature against unrecoverable communication failures or radio degradation due to jamming cyber-attacks is the ability for autonomous systems to robustly execute their mission with onboard perception. This is especially true for marsupial air / ground teams, wherein landing onboard the ground robot is required. We propose a pipeline that relies on Deep Neural Network-based Vehicle-to-Vehicle detection based on aerial views acquired by flying at typical altitudes for Micro Aerial Vehicle-based real-world surveying operations, such as near the border of the 400ft Above Ground Level window. We present the minimal computing and sensing suite that supports its execution onboard a fully autonomous micro-Tiltrotor aircraft which detects, approaches, and lands onboard a Boston Dynamics Spot legged robot. We present extensive experimental studies that validate this marsupial aerial / ground robot’s capacity to safely reassemble while in the airborne scouting phase without the need for wireless communication.  more » « less
Award ID(s):
2150394
NSF-PAR ID:
10491759
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
International Conference on Unmanned Aircraft Systems
Page Range / eLocation ID:
626 to 633
Format(s):
Medium: X
Location:
Warsaw, Poland
Sponsoring Org:
National Science Foundation
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