Disassembly Sequence Evaluation: A User Study Leveraging Immersive Computing Technologies

As interest in product recovery, reuse, and recycling rises, planning and evaluating disassembly sequences are becoming increasingly important. The manner in which a product can be taken apart strongly influences end-of-life (EOL) operations and costs. Early disassembly planning can also inform non-EOL processes including repair and routine maintenance. Recently, research has concentrated on creating optimization algorithms which automatically generate disassembly sequences. These algorithms often require data that are unavailable or estimated with high uncertainty. Furthermore, industries often employ CAD modeling software to evaluate disassembly sequences during the design stage. The combination of these methods result in mathematically generated solutions, however, the solutions may not account for attributes that are difficult to quantify (human interaction). To help designers better explore and understand disassembly sequence opportunities, the research presented in this paper combines the value of mathematical modeling with the benefits of immersive computing technologies (ICT) to aid in early design decision making. For the purposes of this research, an ICT application was developed. The application displays both 3D geometry of a product and an interactive graph visualization of existing disassembly sequences. The user can naturally interact with the geometric models and explore sequences outlined in the graph visualization. The calculated optimal path can be highlighted allowing the user to quickly compare the optimal sequence against alternatives. The application has been implemented in a three wall immersive projection environment. A user study involving a hydraulic pump assembly was conducted. The results suggest that this approach may be a viable method of evaluating disassembly sequences early in design

The Generalized A* Architecture

We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A* search and heuristics derived from abstractions to a broad class of lightest derivation problems. We also describe a new algorithm that searches for lightest derivations using a hierarchy of abstractions. Our generalization of A* gives a new algorithm for searching AND/OR graphs in a bottom-up fashion. We discuss how the algorithms described here provide a general architecture for addressing the pipeline problem --- the problem of passing information back and forth between various stages of processing in a perceptual system. We consider examples in computer vision and natural language processing. We apply the hierarchical search algorithm to the problem of estimating the boundaries of convex objects in grayscale images and compare it to other search methods. A second set of experiments demonstrate the use of a new compositional model for finding salient curves in images

An efficient algorithm for searching implicit AND/OR graphs with cycles

We present an efficient AO*-like algorithm that handles cyclic graphs without neither unfolding the cycles nor looping through them. Its top-down search strategy is based on Mahanti and Bagchi's CF, whereas its bottom-up revision process is inspired in Chakrabarti's REV*. However, important modifications have been introduced in both algorithms to attain a true integration and gain efficiency. Proofs of correctness and completeness are included. Up to our knowledge, the resulting algorithm --called CFC REV*-- is the most efficient one available for this problem.This work was supported by the project 'Computación mediante restricciones en robótica y gestión de recursos' (070-725). CICYT contracts TIC96-0721-C02-01 and TAP99-1086-C03-01.Peer Reviewe