Search

Destructive disasters can cause enormous harm and losses, and due to a lack of information for a certain period of time after the disaster, it is extremely detrimental to understanding the disaster situation and disaster relief. In order to obtain timely collapse and damage information of buildings, roads, bridges, reservoirs and other important ground objects in disaster areas, a method for automatically identifying and annotating the differences in remote sensing images before and after disasters has been proposed. Firstly, Gaussian noise was removed from time-series remote sensing images using block matching 3D (BM3D) method, and then image registration was performed using scale invariant feature transform (SIFT) method. The edge information of important ground objects in different areas was obtained by using Wv_Canny edge detection method for difference images. Finally, the important ground objects that have changed were identified and annotated. The experimental results of remote sensing image were compared according to the change area of the building, the correct rate is 78% ~ 79%, the false detection rate is 21% ~ 22%, and there is no missed detection rate. Simulation experiment and actual remote sensing image processing show that the proposed method can effectively identify and mark the difference areas of important ground objects such as buildings, which is conducive to timely understanding and rescue of destructive ground objects after disaster. [ABSTRACT FROM AUTHOR]

Most of the crabs are fed manually at present in China. However, manual feeding cannot fully meet the industrial requirements of mass production, particularly with an increasingly aging labor force. Fortunately, automatic navigation can be expected to promote the development of unmanned feeding boats. This study aims to realize the real-time path planning of an unmanned feeding boat in a crab pond environment with static and dynamic obstacles. A dynamic path planning was proposed for the unmanned feeding boat using the fusion of the improved A* and the artificial potential field (FIA*-APF). Better performance was achieved to solve the excessive turning points, large path curvature, and numerous redundant points in the traditional A* path planning. Firstly, the evaluation function was optimized to find the end of the path. Then, the turn penalty function was introduced to remove the redundant points for the smaller number of turning points. The path was then smoothed using a B-spline curve. Finally, the gravitational and repulsive functions of the artificial potential field were improved to obtain the global paths using the improved A*. The gravitational paths were used to treat the local optimization and unreachability in the artificial potential field. Two aquaculture pond environments with static and dynamic obstacles were established to evaluate the performance of the traditional artificial potential field (TAPF), the fusion with the A* and artificial potential field (TA*- APF), as well as the FIA*-APF. Planning speed, number of nodes and path length were selected as the evaluating indicators. Each experiment was carried out 20 times in every single environment. The simulation results showed that the average planning time in the two environments was 17.23% and 51.96%, respectively, compared with the TAPF and the TA*-APFs. Furthermore, the average node numbers were 50.64% and 65.03% less than that, respectively. The average path length was 2.82% less than the TA*-APF. The results of 10 crab pond tests showed that the planning time was only 38.16% and 62.42% of the TAPF and the TA*-APFs, respectively. The average path lengths were 29.13% and 10.15% less than that, respectively. In addition, the TAPF and TA*-APFs planned paths with corners greater than 60° were 3.28 and 2.62 times greater than FIA*- APF, while the corners greater than 100° were 3.73 and 1.67 times greater than FIA*-APF, respectively. The path planned was safer and smoother after optimization. The finding can provide a sound reference for the navigation development in unmanned feeding boats. [ABSTRACT FROM AUTHOR]