The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).
The mobile crushing station is one of the main equipment of the semi-continuous open-pit mining system. The discharge arm and the receiving equipment are manually aligned, which has the problems of long alignment time and low alignment accuracy, which affects the working efficiency of the mining system. According to the development and application of space rendezvous and docking technology at home and abroad, the advantages and disadvantages of different measurement methods are compared and analysed, and the method of applying binocular measurement technology to system positioning in the automatic alignment system of the discharge arm is determined. There are three movements in the mechanical part of the designed discharge arm alignment control system, including the rotary motion of the visual measurement mechanism and the horizontal rotation and telescopic motion of the discharge arm. According to the kinematic analysis and binocular vision measurement theory, the deviation model of the alignment control of the discharge arm is established. A binocular vision measurement and localization method based on the combination of stereo calibration and template matching is proposed, which achieves surprising measurement accuracy. An automatic alignment method of the discharge arm of the mobile crushing station is proposed based on the binocular vision and fuzzy control method. Its validity is verified by simulation and experiment. The strategy of motion decomposition is applied to the alignment system to avoid unnecessary motion of the discharge arm. The research results all show that the alignment method can achieve the angle deviation within ±0.5 degrees, the distance deviation within ±15 mm, and the test alignment time is about 5 minutes, which is better than other alignment control models; the alignment accuracy and the alignment time are improved by more than 50%. The method can control the discharge arm to complete the alignment task quickly and smoothly, which lays a foundation for the further automatic research of the discharge arm.
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