Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques are useful in solving a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways of classifying various efforts in literature for surface defect detection using deep learning techniques. These three ways are based on defect detection context, learning techniques, and defect localization and classification method respectively. This article also identifies future research directions based on the trends in the deep learning area.

Mobile manipulators that combine base mobility with the dexterity of an articulated manipulator have gained popularity in numerous applications ranging from manufacturing and infrastructure inspection to domestic service. Deployments span a range of interaction tasks with the operational environment comprising minimal interaction tasks such as inspection and complex interaction tasks such as logistics resupply and assembly. This flexibility, offered by the redundancy, needs to be carefully orchestrated to realize enhanced performance. Thus, advanced decision-support methodologies and frameworks are crucial for successful mobile manipulation in (semi-) autonomous and teleoperation contexts. Given the enormous scope of the literature, we restrict our attention to decision-support frameworks specifically in the context of wheeled mobile manipulation. Hence, here, we present a classification of wheeled mobile manipulation literature while accounting for its diversity. The intertwining of the deployment tasks, application arenas, and decision-making methodologies are discussed with an eye for future avenues for research.

Conventional material extrusion additive manufacturing (AM) processes require the user to make a trade-off between surface quality and build time of the part. A large bead filament deposition can speed up the build process; however, it leads to surfaces with high roughness due to the stair-stepping effect. The surface quality can be improved by using a small bead filament deposition, which increases the build time of the part. We present a new approach incorporating hybrid multi-resolution layers in material extrusion additive manufacturing to provide excellent surface quality without increasing the build time. Our slicing algorithm generates planar layers with large filaments to fill the interior regions in less time. The generated exterior layers are conformal and use small filaments to reduce the stair-stepping effect and improve surface quality. We also present a path planning algorithm to build parts with a single manipulator using a multi-nozzle extrusion tool. The path planning algorithm generates a smooth material deposition path by avoiding collision between the tool and the already built layers. It reduces the collision checks and performs collision detection in a computationally efficient manner. We build five parts to validate our approach and illustrate the benefits of multi-resolution AM.

Three-dimensional reconstruction technology is used in a wide variety of applications. Automatically creating accurate pointclouds for large parts with complex geometries usually requires expensive metrology instruments. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture pointclouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.

Tool-Path planning is the foundation for automating subtractive/additive manufacturing processes. Robotic manipulators are increasingly being considered to automate tasks that require complex tool motions. Manipulators provide extra degrees of freedom and are more flexible than traditional automation technologies. However, a tool-path needs to be planned and given to the

Tool-Path planning is the foundation for automating subtractive/additive manufacturing processes. Robotic manipulators are increasingly being considered to automate tasks that require complex tool motions. Manipulators provide extra degrees of freedom and are more flexible than traditional automation technologies. However, a tool-path needs to be planned and given to the