Machine Learning-Based Vision Mapping and Planning for Flexibility in High-Mix Robotic Manufacturing

<div class="section abstract"><div class="htmlview paragraph">Industries that require high-accuracy automation in the creation of high-mix/low-volume parts, such as aerospace, often face cost constraints with traditional robotics and machine tools due to the need for many pre-programmed tool paths, dedicated part fixtures, and rigid production flow. This paper presents a new machine learning (ML) based vision mapping and planning technique, created to enhance flexibility and efficiency in robotic operations, while reducing overall costs. The system is capable of mapping discrete process targets in the robot work envelope that the ML algorithms have been trained to identify, without requiring knowledge of the overall assembly. Using a 2D camera, images are taken from multiple robot positions across the work area and are used in the ML algorithm to detect, identify, and predict the 6D pose of each target. The algorithm uses the poses and target identifications to automatically develop a part program with efficient tool paths, including accommodations for the different processes required by each identified target type. For higher-accuracy processes, the initial camera-based location estimates are refined using a 3D structured light scanner. The same sensor can be used to perform post-process inspection, detecting deviations-from-nominal in the scan data to ensure process quality. When implemented on mobile stations with collaborative robots, these techniques enable systems to be transported where they are most needed on the manufacturing floor and to work alongside operators. When used together, these developments give the system significant advantages over traditional methods: increased flexibility in part and robot placement, improved efficiency through reduced setup times, adaptability in the targets being processed, and scalability to accommodate various operations. By eliminating the constraints of rigid pre-programmed setups, this ML-based vision mapping and planning system offers a novel solution that expands robotic capabilities in automated manufacturing.</div></div>