One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots' camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175354PMC
http://dx.doi.org/10.3390/s24113494DOI Listing

Publication Analysis

Top Keywords

camera targeting
12
mad robot
12
low-cost mad
8
ardurover firmware
8
power equipment
8
equipment inspection
8
power plants
8
targeting
6
power
5
gnss-based narrow-angle
4

Similar Publications

Vehicle speed measurement method using monocular cameras.

Sci Rep

January 2025

Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.

This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed.

View Article and Find Full Text PDF

Creating coveted bioluminescence colors for simultaneous multi-color bioimaging.

Sci Adv

January 2025

Department of Biomolecular Science and Engineering, SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan.

Bioluminescence, an optical marker that does not require excitation by light, allows researchers to simultaneously observe multiple targets, each exhibiting a different color. Notably, the colors of the bioluminescent proteins must sufficiently vary to enable simultaneous detection. Here, we aimed to introduce a method that can be used to expand the color variation by tuning dual-acceptor bioluminescence resonance energy transfer.

View Article and Find Full Text PDF

Maintaining a healthy population of common leopards, a highly adaptive felid, requires updated information on their spatial occurrence. In Nepal's Tarai region, leopards coexist with tigers, which are well-studied felid throughout its range. However, knowledge is very scarce on the patterns of leopard occupancy.

View Article and Find Full Text PDF

In targeted alpha-particle therapy, actinium-225 (Ac-225) has emerged as a radionuclide of potential, driving extensive efforts to develop innovative radiopharmaceuticals. High-resolution imaging of alpha particles is required for precisely detecting alpha-emitting radionuclides in cellular environments and small organs. Here, we report real-time trajectory imaging of alpha particles emitted by Ac-225 and its daughter radionuclides, utilizing an alpha particle trajectory imaging system.

View Article and Find Full Text PDF

Target tracking techniques in the UAV perspective utilize UAV cameras to capture video streams and identify and track specific targets in real-time. Deep learning UAV target tracking methods based on the Siamese family have achieved significant results but still face challenges regarding accuracy and speed compatibility. In this study, in order to refine the feature representation and reduce the computational effort to improve the efficiency of the tracker, we perform feature fusion in deep inter-correlation operations and introduce a global attention mechanism to enhance the model's field of view range and feature refinement capability to improve the tracking performance for small targets.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!