Abstract
Examining improvised explosive devices (IEDs) and chemical, biological, radiological, and nuclear (CBRN) threats presents immediate risks to the personnel and surrounding areas. Remote-controlled robots are crucial for approaching potentially hazardous objects safely. However, current camera-assisted manual control methods hinder precise manoeuvring, especially in unknown complex environments without a direct line of sight to the robot. Additionally, navigating into the target area and examining suspicious objects at close distances are often time-consuming and resource-intensive tasks. We present a semi-automatic robotic system based on the Rosenbauer RTE Robot tracked platform equipped with an industrial-grade robotic arm. It is composed of Commercial-off-the-Shelf (COTS) components and is designed to minimise the operational burden during manoeuvring and robotic arm movements. The robot is equipped with LiDAR sensors, a GNSS receiver, an IMU, rotary encoders, and a Time-of-Flight (TOF) camera.
These navigation sensors facilitate semi-autonomous operations. The system is capable of semi-autonomous navigation in both indoor and outdoor environments. A multi-sensor SLAM algorithm based on Factor Graph Optimisation (FGO) is used to construct a 3D point cloud of the environment. Via a user interface, an operator can set waypoints in this point cloud to which the robot navigates autonomously. As the robot navigates, it continuously scans its environment to avoid obstacles and to extend the map of the surrounding area. Furthermore, close-up inspection and surface scans are executed semi-autonomously. We use spline draping to wrap primitive user inputs around 3D shapes obtained from on-board 3D sensors. In addition, scan occlusions in the surface scan area are detected and closed automatically. This facilitates equidistant scan patterns for contamination monitoring using e.g. a hyperspectral imager or alpha-radiation detectors.
These navigation sensors facilitate semi-autonomous operations. The system is capable of semi-autonomous navigation in both indoor and outdoor environments. A multi-sensor SLAM algorithm based on Factor Graph Optimisation (FGO) is used to construct a 3D point cloud of the environment. Via a user interface, an operator can set waypoints in this point cloud to which the robot navigates autonomously. As the robot navigates, it continuously scans its environment to avoid obstacles and to extend the map of the surrounding area. Furthermore, close-up inspection and surface scans are executed semi-autonomously. We use spline draping to wrap primitive user inputs around 3D shapes obtained from on-board 3D sensors. In addition, scan occlusions in the surface scan area are detected and closed automatically. This facilitates equidistant scan patterns for contamination monitoring using e.g. a hyperspectral imager or alpha-radiation detectors.
Originalsprache | Englisch |
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Titel | Autonomous Systems for Security and Defence |
Redakteure/-innen | Judith Dijk, Jose Luis Sanchez-Lopez |
Seitenumfang | 14 |
Band | 13207 |
Publikationsstatus | Veröffentlicht - 13 Nov. 2024 |
Veranstaltung | SECURITY + DEFENCE - Edinburgh, Edinburgh, Großbritannien/Vereinigtes Königreich Dauer: 16 Sept. 2024 → 20 Sept. 2024 |
Konferenz
Konferenz | SECURITY + DEFENCE |
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Land/Gebiet | Großbritannien/Vereinigtes Königreich |
Stadt | Edinburgh |
Zeitraum | 16/09/24 → 20/09/24 |
Research Field
- Responsive Sensing & Analytics