Radar-based object detection at drones and underground facilities for enhanced environmental perception in Indian mines

Abstract: This research paper presents an innovative approach to object detection in drones utilizing the HB100 radar module. The primary objective of this study was to develop a system capable of capturing both stationary and moving objects, regardless of the visibility conditions in the surrounding environment. By integrating the HB100 radar module into the drone’s architecture, real-time object detection and tracking capabilities were achieved, significantly enhancing the drone’s perception abilities. The experimental setup involved integrating the HB100 radar module for a quad-copter drone external platform linked to the base station; enabling the collection of radar signals and subsequent processing for object detection. Various stationary and moving objects were employed to evaluate the effectiveness of the system under different environmental conditions, including scenarios with limited visibility, such as fog or darkness and power-bandwidth. The HB100 radar module operates based on the principle of Doppler radar. It emits continuous wave (CW) signals and detects the Doppler shift in the reflected signals to 1(zero crossing). This article motivates on the RGB Charged Coupled Device or night vision camera and Synthetic Aperture Radar (SAR) integration at UAV or drone platform. The on board Arduino micro-controller trigger the external circuit of the airborne transceiver. The modular change of the COFFE CAN radar digital signal processing (DSP) and HB100 radar infer the omni-direction antenna piece out with the directional DSP. As a result, the wide sense stationary (WSS) signal forms image of inner-wall and outer wall but clinging on syphoned based diffractive medium caused on deliverable environmental conditions. The changing to standing wave in a reflecting wall distance or determine the presence and movement of objects. The safety and mine hazard conditions the flooding and double differentiating checks it accelerating values on positive and negative sides forming colored pattern of moving directional object movement. The phase patterns of these images are the region of convergence using Z transform on those images changing values to spirals. Lastly, the issues of whole setup is tested with load balancing on both on weight and electric circuits DC due to not operating shunting based on electron discharge phenomenon in any environmental conditions.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-30
DOI
https://doi.org/10.5281/zenodo.14021024
Primary Topic
Geophysical Methods and Applications
Type
article
Field-Weighted Citation Impact
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article

Radar-based object detection at drones and underground facilities for enhanced environmental perception in Indian mines

Zenodo (CERN European Organization for Nuclear Research)
Geophysical Methods and Applications
article

Radar-based object detection at drones and underground facilities for enhanced environmental perception in Indian mines

article en

Abstract

Abstract: This research paper presents an innovative approach to object detection in drones utilizing the HB100 radar module. The primary objective of this study was to develop a system capable of capturing both stationary and moving objects, regardless of the visibility conditions in the surrounding environment. By integrating the HB100 radar module into the drone’s architecture, real-time object detection and tracking capabilities were achieved, significantly enhancing the drone’s perception abilities. The experimental setup involved integrating the HB100 radar module for a quad-copter drone external platform linked to the base station; enabling the collection of radar signals and subsequent processing for object detection. Various stationary and moving objects were employed to evaluate the effectiveness of the system under different environmental conditions, including scenarios with limited visibility, such as fog or darkness and power-bandwidth. The HB100 radar module operates based on the principle of Doppler radar. It emits continuous wave (CW) signals and detects the Doppler shift in the reflected signals to 1(zero crossing). This article motivates on the RGB Charged Coupled Device or night vision camera and Synthetic Aperture Radar (SAR) integration at UAV or drone platform. The on board Arduino micro-controller trigger the external circuit of the airborne transceiver. The modular change of the COFFE CAN radar digital signal processing (DSP) and HB100 radar infer the omni-direction antenna piece out with the directional DSP. As a result, the wide sense stationary (WSS) signal forms image of inner-wall and outer wall but clinging on syphoned based diffractive medium caused on deliverable environmental conditions. The changing to standing wave in a reflecting wall distance or determine the presence and movement of objects. The safety and mine hazard conditions the flooding and double differentiating checks it accelerating values on positive and negative sides forming colored pattern of moving directional object movement. The phase patterns of these images are the region of convergence using Z transform on those images changing values to spirals. Lastly, the issues of whole setup is tested with load balancing on both on weight and electric circuits DC due to not operating shunting based on electron discharge phenomenon in any environmental conditions.

Zenodo (CERN European Organization for Nuclear Research)
Indian Institute of Technology Kharagpur (IN), Institute of Environmental Studies & Wetland Management (IN), KIIT University (IN), Indian Institute of Technology Delhi (IN)
Life in Land
Openalex Percentile: Top 59%
Geophysical Methods and Applications
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