A Hybrid Sensor-Fusion System to Locate the Electric Gridlines by UAV for Range Extension in Urban Areas

2022-26-0007

05/26/2022

Event
AeroCON 2022
Authors Abstract
Content
This paper explores the efficacy and efficiency of a system for the effective location of electric gridlines during daytime and night-time by the onboard and offboard transceivers of UAV through vehicle to infrastructure communication.
The usage of electric gridlines in urban areas helps to extend the range of the UAVs by charging the onboard battery using an extended arm. The same arm can also be used for direct propulsion of the motors onboard UAV, thereby minimizing the reliance on battery.
UAVs with advanced Image processing algorithms are utilized in the inspection of the electric grid lines themselves in the Power industry. The camera based algorithms are not effective during night-time when the gridlines are near invisible. This can be mitigated by evaluating light in other spectral ranges, but this would add to the load of the UAV.
We propose a system which combines multiple information sources and helps locate the gridlines for range extension, specifically for the delivery of packages in the Urban Mobility domain. The system utilizes annotated maps for locating any electric grid lines in the vicinity. The finer control needed for placing the extension arm on live electric wire is done using a set of three radio transceivers installed on an electric pole and a double or triple transceiver configuration onboard UAV which locates the live-wire through deductive analysis of sensor data. The trajectory planning subsystem can utilize this information for establishing an efficient route and make multiple deliveries.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-26-0007
Pages
13
Citation
Pappala, L., Enagandula, S., and Manoharan, S., "A Hybrid Sensor-Fusion System to Locate the Electric Gridlines by UAV for Range Extension in Urban Areas," SAE Technical Paper 2022-26-0007, 2022, https://doi.org/10.4271/2022-26-0007.
Additional Details
Publisher
Published
May 26, 2022
Product Code
2022-26-0007
Content Type
Technical Paper
Language
English