lj fan shaaacn h ga aaccn Abstract-This paper presents an obstacle-navigation strategy for a wire-suspend robot for power transmission lines based on a novel movement mechanism. The kinematics of the mobile robot and the process of obstacle-navigation control are analyzed in detail. A realtime expert system with hybrid architecture C Language Integrated Production System (CLIPS) is described as the mobile robot control system. The realtime expert system is developed by CLIPS and C language, so it has powerful logical reasoning ability and data processing ability. And the expert system can instruct the mobile robot to navigate obstacles autonomously on overhead ground wires. A realtime process and a conflictelimination algorithm realize the realtime ability of the control system. The robot task plan has two modes: layered plan and direct control. The robot can study online or offline, and when it encounter the same obstacles, it can navigate them quickly.

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In the field of aerial image object detection based on deep learning, it’s difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it’s hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.