Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Automatic Robot Path Planning for Visual Inspection from Object Shape

Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined inspection poses and training images essential for the training of models. The acquisition of such data and their integration into an inspection framework is challenging due to the variety in objects and scenes involved and due to additional bottlenecks caused by the manual collection of training data by humans, thereby hindering the automation of visual inspection across diverse domains. This work proposes a solution for automatic path planning using a single depth camera mounted on a robot manipulator. Point clouds obtained from the depth images are processed and filtered to extract object profiles and transformed to inspection target paths for the robot end-effector. The approach relies on the geometry of the object and generates an inspection path that follows the shape normal to the surface. Depending on the object size and shape, inspection paths can be defined as single or multi-path plans. Results are demonstrated in both simulated and real-world environments, yielding promising inspection paths for objects with varying sizes and shapes. Code and video are open-source available at: https://github.com/CuriousLad1000/Auto-Path-PlannerComment: 8 page

Repair of metallic components using hybrid manufacturing

Many high-performance metal parts users extend the service of these damaged parts by employing repair technology. Hybrid manufacturing, which includes additive manufacturing (AM) and subtractive manufacturing, provides greater build capability, better accuracy, and surface finish for component repair. However, most repair processes still rely on manual operations, which are not satisfactory in terms of time, cost, reliability, and accuracy. This dissertation aims to improve the application of hybrid manufacturing for repairing metallic components by addressing the following three research topics. The first research topic is to investigate and develop an efficient best-fit and shape adaption algorithm for automating 3D models\u27 the alignment and defect reconstruction. A multi-feature fitting algorithm and cross-section comparison method are developed. The second research topic is to develop a smooth toolpath generation method for laser metal deposition to improve the deposition quality for metallic component fabrication and repair. Smooth connections or transitions in toolpath planning are achieved to provide a constant feedrate and controllable deposition idle time for each single deposition pass. The third research topic is to develop an automated repair process could efficiently obtain the spatial information of a worn component for defect detection, alignment, and 3D scanning with the integration of stereo vision and laser displacement sensor. This dissertation investigated and developed key technologies to improve the efficiency, repair quality, precision, and automation for the repair of metallic components using hybrid manufacturing. Moreover, the research results of this dissertation can benefit a wide range of industries, such as additive manufacturing, manufacturing and measurement automation, and part inspection --Abstract, page iv

Saliency-guided Adaptive Seeding for Supervoxel Segmentation

We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201

Trajectory and spray control planning on unknown 3D surfaces for industrial spray painting robot

Automated 3D path and spray control planning of industrial painting robots for unknown target surfaces is desired to meet demands on the production system. In this thesis, an image acquisition and laser range scanning based method has been developed. The system utilizes the XY projection of the boundaries of the target surface to generate the gun trajectory\u27s X and Y coordinates as well as the spray control. Z coordinates and gun direction, distance, and speed are generated based on the point cloud from the target that is acquired by the laser scanner. A simulation methodology was also developed which is capable of calculating the paint thickness across the target surface. Results have shown that the generated path could perform a full coverage on the target surface, while keeping the paint material waste at the minimum. Excellent paint thickness control could be achieved on 2D and straight line sweep surfaces, while a satisfactory thickness is obtained on other 3D arbitrary surfaces. Relationships among thickness, spray deposition profile, sampling roughness and geometric features of the target surfaces have been discussed to make this method more applicable in industry