Threaded fasteners are prevalent throughout modern manufacturing. Thus, as the demand for automation in manufacturing increases, so does the demand for automated threaded fastening systems. However, many fundamental issues and engineering challenges still hinder robustness in automation, particularly for smaller screws and critical product finishing requirements. This paper surveys the state of the art in threaded fastening automation and discusses open questions for further research. This survey covers the following areas: 1) fundamentals of threaded fastening, including basic concepts and definitions; 2) analysis of the entire assembly process (consisting of part feeding and orientation, pickup, alignment, and driving), including discussions of tools, control strategies, and other considerations; 3) failure modes and techniques to mitigate them; 4) threaded fastening systems and electromechanical approaches; and 5) open challenges and suggestions for future development. Understanding the current state of automation in threaded fastening will provide a foundation for researchers to advance this field.

Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly line with far more flexibility. An assembly line made up of mobile manipulators can easily and rapidly be reconfigured to support scalability and a varied product mix, while allowing for near optimal resource assignment. The challenge to using small robots in place of monuments is making their joint behavior precise enough to accomplish the task and efficient enough to execute subtasks in a reasonable period of time. In this paper, we describe a set of techniques that we combine to achieve the necessary precision and overall efficiency to build a large structure. We describe and demonstrate these techniques in the context of a testbed we implemented for assembling a wing ladder.

In the past, robotic assembly has required rigid fixturing and special purpose robotic tools for every assembly component. Unfortunately, rigid fixtures and special purpose robotic tools often have to be customized for varying geometries. Alternatively, it is possible to operate in a semi-structured environment, defined by the use of softer fixtures (e.g. pickup bin) and softer robotic tools (e.g. suction cups or compliant pads) that can be used for many assembly applications without modification, but they demand specific motion plans that can tolerate greater positional uncertainty. We have developed a system that supports autonomous generation of parameterized fine motion plans for assembly that are robust under positional uncertainty and compliance introduced by the use of a suction cup instead of a gripper. To accomplish this a classifier is trained, implemented and tested for performance in the semi-structured environment for distinguishing between a failed or successful assembly. The trained classifier is then integrated with the entire system and many robot-attended experiments are performed that vary the fine motion parameters, and optimize them for successful outcomes using an Interval Estimation optimization algorithm. An approach to machine learning based on Support Vector Machines and Principal Component Analysis is used to make the optimization autonomous. We achieved a 99.7% classification accuracy with the trained classifier and by running repeated robot-attended experiments with artificial positional uncertainty and optimizing fine motion parameters, we were able to achieve a 38% improvement compared to fine motion plans with initial best guess parameters.

This paper uses a configuration space (c-space) based method to compute interference-free configuration for stacking polyhedral sheet metal parts. This work forms the interference analysis module of a stacking planner developed by us. Parts in a stack should not interfere with each other and should also satisfy stability, grasping, and stacking plan feasibility related constraints. We present two techniques to speed up the expensive step of c-space obstacle computation. The first technique identifies orientation intervals (for a convex pair of solids) within which the topology of face-edge-vertex graph of an obstacle stays the same. Within this interval, c-space obstacle geometry for one orientation can be extrapolated from obstacle geometry for another orientation. Our experiments show that extrapolation takes an order of magnitude less than the time taken to compute an obstacle from scratch. The second technique computes near optimal interference-free positions for a discrete orientation without having to compute the complete c-space obstacle. Our experiments show that, for complex sheet metal parts, less than 0.1% of the convex component pairs are evaluated in order to compute an interference-free configuration. We describe a configuration space-based method to compute a list of interference-free configurations that can be tested to see if they satisfy the above mentioned constraints. The cost function is a weighted sum of components that penalize floor space utilization and height of center of gravity of parts. The algorithm is able to pick nested stacks that tend to be stable and compact without having to explicitly enumerate features that can be nested. It is also able to accommodate flanges in holes to reduce the value of the user specified cost function. We use three test parts to illustrate the effect of the two techniques to speed up c-space obstacle computation. We also show the stacking plans generated for three different values of the weighting parameter in the cost function used by the stacking planner.

Process planning is a time-consuming and tedious operation that is necessary to manufacture products. In addition, process plans must be optimized to drive machines most productively. To accomplish this goal, the authors search for plans that satisfy production feasibility requirements and time constraints. Unfortunately, this search operates in an enormous search-space that is usually impossible to navigate. Therefore, the authors use features and their corresponding heuristics to guide the search. To make the search more tractable, the authors convert some features into corresponding constraints as a priori knowledge to prune the space. The applications of these features are presented in this paper. The authors also discuss the feature interaction problems in the process planning. In this paper, the authors focus on the production of bent sheet metal parts and the machine setup. The authors have designed over forty test parts with their "parallel design" system, and the features are automatically generated as the design progresses. After the design is complete, an automatic process planning system uses the features and generates new ones to aid the production of a near-minimum manufacturing cost plan. Finally, these plans are used to produce parts on an automatic bending system.