With the rapid development of smart grid technology, power inspection work is facing unprecedented challenges and opportunities. Traditional inspection methods are not only inefficient, but also difficult to cover com...
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ISBN:
(纸本)9798350375145;9798350375138
With the rapid development of smart grid technology, power inspection work is facing unprecedented challenges and opportunities. Traditional inspection methods are not only inefficient, but also difficult to cover complex power environments. As an efficient and flexible inspection tool, drones have gradually become an important force in the field of power inspection. However, in the process of power inspection, due to the complex and ever-changing environment and numerous obstacles, how to achieve autonomous obstacle avoidance for drones has become an urgent problem to be solved. This paper proposes an autonomous obstacle avoidance method for unmanned aerial vehicles based on multi-sensor fusion. By using an improved bayesian fusion algorithm, the point cloud information obtained from two-dimensional LiDAR and depth camera is fused to compensate for the shortcomings of a single sensor in detecting complex structures of power lines. The experimental results show that this method significantly improves the perception accuracy of drones in the surrounding environment during power inspection, with a root mean square error of less than 0.05m for fused point clouds, and has good obstacle avoidance effect. During the testing process, the distance between the drone and obstacles was maintained at 0.5m or more, ensuring the safe flight of the drone during power inspection and providing effective technical support for the inspection work of the smart grid.
For more exact navigation and positioning on the navigation systems, this paper discusses the improvement of statistical algorithms based on fuzzy knowledge with a brand-new theory. The improved fusionalgorithms base...
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For more exact navigation and positioning on the navigation systems, this paper discusses the improvement of statistical algorithms based on fuzzy knowledge with a brand-new theory. The improved fusionalgorithms based on bayesian theory and fuzzy methods on the track recognition are also given, respectively. However, this paper mainly makes a comparison of the improved new algorithms and two old algorithms for track recognition. The computer simulation shows the validity and feasibility of two improved algorithms. At the same time, the simulation results show that the overall performance of two improved algorithms for the track recognition all is better than that of the probabilistic algorithms. Based on the experiments in the dense target environment, the correct average recognition rate of statistical algorithms is 66.96% at the highest;that of the improved new algorithm based on fuzzy knowledge is 77.98%, that of the bayesian fusion algorithm is 86.75%, however, that of the fuzzy fusionalgorithm can be 90.14%. These results support the usefulness of the two improved algorithms. (C) 2002 Elsevier Science. All rights reserved. (C) 2008 Elsevier Inc. All rights reserved.
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