Simultaneous Localization and Map construction (SLAM) is a hot research topic in mobile robotics and is a crucial technology for developing inspection robots. This paper focuses on mapping the environment by analyzing the Gmapping and Karto Slam algorithms' mapping effects and constructing a simulation environment map using the Karto Slam algorithm. To address the obstacle avoidance problem that cannot be handled by only using the Dijkstra algorithm for global path planning, an improved DWA algorithm is introduced for local path planning to optimize the real-time obstacle avoidance function of the robot. Based on the construction of a robot simulation platform, this paper studies the previous simulation of an autonomous inspection robot before actual experiments. Through simulation experiments of mapping and path planning, this study provides support for the feasibility of robot field experiments, reducing the cycle and difficulty of on-site debugging.
The main objective of this paper is to design and research a vision-based positioning system for robotic wire cutting to address the problems of small iron wire pixels and not easy recognition. This system improves the accuracy of wire detection by improving the YOLOv5 algorithm for small target detection, setting up a new gradient path on the SPP, and enhancing the feature extraction capability. This paper uses a FANUC robot as the robot platform, and a clip-shear device is designed to fix the wire to achieve automatic shearing. Experimental results show that this system can accurately detect and locate the wire and effectively perform automated shearing.
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