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Applications of GNSS
Published in Basudeb Bhatta, Global Navigation Satellite Systems, 2021
There are many types of unmanned vehicles, including ground vehicles, aerial vehicles, underwater vehicles, and surface vehicles. Unmanned heavy equipment vehicles on the ground can use GNSS in construction, mining, and precision agriculture. GNSS technology is being integrated into equipment such as bulldozers, excavators, graders, pavers, and farm machinery to enhance productivity in the real-time operation of these equipment. The blades and buckets of construction equipment are controlled automatically by GNSS-based machine guidance systems (Ryan and Popescu 2006). Agricultural equipment may use GNSS to steer automatically or as a visual aid displayed on a screen for the driver. This is very useful for controlled traffic, row crop operations, and when spraying. Harvesters with yield monitors can also use GNSS to create a yield map of the paddock being harvested.
Optimal Path Planning of an Unmanned Surface Vehicle in a Real- Time Marine Environment using a Dijkstra Algorithm
Published in Adam Weintrit, Marine Navigation, 2017
Y. Singh, S. Sharma, R. Sutton, D. Hatton
With the growing advances in navigation technologies, there is a greater need to explore oceans for resources as well as for the future needs. Autonomous unmanned vehicles have shown the potential towards various missions of scientific and military significance depending upon the requirement, environment and cost involved (Serreze et al., 2008 and Legrand et al., 2003). Unmanned vehicles can be classified into four categories namely, unmanned aerial vehicles (UAVs), unmanned underwater vehicles (UUVs), unmanned ground vehicles (UGVs) and unmanned surface vehicles (USVs). USVs are watercraft of small (<1 tonnes) or medium (100 tonnes) size in terms of water displacement.
Operations on the Move: Vehicle Movement and Soldier Performance
Published in Pamela Savage-Knepshield, John Martin, John Lockett, Laurel Allender, Designing Soldier Systems, 2018
Increasingly, the military is employing unmanned vehicles, such as the aerial Predator and the small unmanned ground robots used to scout for improvised explosive devices in Iraq and Afghanistan. Teleoperated systems provide the advantage of allowing soldiers to explore and affect areas of interest without exposing themselves to risk and, as robotic navigation systems improve, may allow one soldier to control multiple vehicles during convoys or routine movement (Muench et al. 2000).
Drag reduction design and research of high-speed amphibious vehicle’s deformable track wheels
Published in Ships and Offshore Structures, 2023
Bolong Liu, Xiaojun Xu, Dibo Pan, Yikun Feng, Shengyang Lu
Amphibious vehicles are designed to pass through the river, sea, and coastal areas. In recent years, amphibious vehicles have been widely used in military and civilian fields (Nakisa et al. 2017). In underdeveloped and post-disaster areas, amphibious vehicles can quickly cross water barriers and carry out transport and rescue missions. On offshore, amphibious vehicles can easily transport people and materials between water and land, effectively saving transition time. The high manoeuverability and carrying capacity of the amphibious vehicle also provide effective support for offshore patrols. The high-speed amphibious vehicle (HSAV) is a kind of light multi-function unmanned vehicle with high mobility on the water. It can carry different loads when passing and working in complex waters (Pan et al. 2021).
Adaptive fully distributed anti-disturbance containment control for heterogeneous multi-agent systems
Published in International Journal of Systems Science, 2023
Hao Wu, Shicheng Huo, Ya Zhang
A heterogeneous unmanned vehicle system is used to verify the availability of the proposed method. Refer to Jiang et al. (2018), the first type of vehicle is the linearised dynamics of the Caltech wireless tested multiple vehicle. The system matrices are as follows: The system matrices of the second type of mobile vehicle are given as The dynamics of the leaders are given with following matrices: Choose . To make Hurwitz, the controller gains are given as Choose vehicles 1, 3, 5, and 7 with dynamics (, , ) and vehicles 2, 4, 6, and 8 with dynamics (, , ). We consider eight mobile vehicles subject to time-varying disturbances and . The initial state of and are randomly given and . The rest of the initial values are specified as zero.
Pedestrian detection based on YOLOv3 multimodal data fusion
Published in Systems Science & Control Engineering, 2022
Cheng Wang, Yuan-sheng Liu, Fei-xiang Chang, Ming Lu
The fusion and detection module obtains stable target category information and spatial location data. First, it is necessary to clip the LIDAR and image data to the same detection area. The LIDAR data are preprocessed using a ground point segmentation algorithm because no ground points are required for target detection. Then, the pedestrian target is detected by the improved YOLOv3 algorithm, and the LIDAR clustering algorithm obtains the minimum 3D bounding box of the target. The rotation and translation matrix are used on the 3D bounding box of the LIDAR data, and the 3D point cloud data are transformed into 2D data in the image coordinate system. Decision level fusion is performed using the discriminant condition. Next, pixel-level fusion is conducted for the target to expand the LIDAR data channel and obtain the target detection results from the camera data. Finally, the fusion results are output to the decision-making and control module of the unmanned vehicle.