Backward assembly planning with DFA analysis

An assembly planning system that operates based on a recursive decomposition of assembly into subassemblies, and analyzes assembly cost in terms of stability, directionality, and manipulability to guide the generation of preferred assembly plans is presented. The planning in this system incorporates the special processes, such as cleaning, testing, labeling, etc. that must occur during the assembly, and handles nonreversible as well as reversible assembly tasks through backward assembly planning. In order to increase the planning efficiency, the system avoids the analysis of decompositions that do not correspond to feasible assembly tasks. This is achieved by grouping and merging those parts that can not be decomposable at the current stage of backward assembly planning due to the requirement of special processes and the constraint of interconnection feasibility. The invention includes methods of evaluating assembly cost in terms of the number of fixtures (or holding devices) and reorientations required for assembly, through the analysis of stability, directionality, and manipulability. All these factors are used in defining cost and heuristic functions for an AO* search for an optimal plan

Sub-assembly partitioning choice for complex assemblies based on an action-count-closure criterion

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaves 65-66).by Stephen J. Rhee.M.S

Generation of Optimized Robotic Assembly Sequence using Soft Computing Methods

The assembly process is one of the most time consuming and expensive manufacturing activities. The cost of assembly on an average is 10-30% of the manufacturing cost of a commercial product. The ratio between cost and performance of assembly has gradually increased with respect to the other phases of the manufacturing process and in recent years, this fact has caused a growing interest by industry in this area. Robotic assembly system which comes under the automated assembly system ncorporates the use of robots for performing the necessary assembly tasks. This is one of the most flexible assembly systems to assemble various parts into desired assembly (usable end-product). Robotic assembly systems are the programmable and have the flexibility to handle a wide range of styles and products, to assemble the same products in different ways, and to recover from errors. Robotic assembly has the advantage of greater process capability and scalability. It is faster, more efficient and precise than any conventional process. A variety of optimization tools are available for application to the problem. It is difficult to model the present problem as an n-p problem. Finding the best sequence generation involves the conventional or soft-computing methods by following the procedures of search algorithms

An Investigation into the Analysis of Truncated Standard Normal Distributions Using Heuristic Techniques

Standard normal distributions (SND) and truncated standard normal distributions (TSND) have been widely used and accepted methods to characterize the data sets in various engineering disciplines, financial industries, medical fields, management, and other mathematic and scientific applications. For engineering managers, risk managers and quality practitioners, the use of the standard normal distribution and truncated standard normal distribution have particular relevance when bounding data sets, evaluating manufacturing and assembly tolerances, and identifying measures of quality. In particular, truncated standard normal distributions are used in areas such as component assemblies to bound upper and lower process specification limits. This dissertation presents a heuristic approach for the analysis of assembly-level truncated standard normal distributions. This dissertation utilizes unique properties of a characteristic function to analyze truncated assemblies. Billingsley (1995) suggests that an inversion equation aids in converting the characteristic functions for a given truncated standard normal distribution to its corresponding probability density function. The heuristic for the inversion characteristics for a single doubly truncated standard normal distribution uses a known truncated standard normal distribution as a probability density function baseline. Additionally, a heuristic for the analysis of TSND assemblies building from the initial inversion heuristic was developed. Three examples are used to further demonstrate the heuristics developed by this dissertation. Mathematical formulation, along with correlation and regression analysis results, support the alternate hypotheses presented by this dissertation. The correlation and regression analysis provides additional insight into the relationship between the truncated standard normal distributions analyzed. Heuristic procedures and results from this dissertation will also serve as a benchmark for future research. This research contributes to the body of knowledge and provides opportunities for continued research in the area of truncated distribution analysis. The results and proposed heuristics can be applied by engineering managers, quality practitioners, and other decision makers to the area of assembly analysis

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications

Modellbasierte automatisierte Greifplanung

Die vorliegende Arbeit beschreibt das Planungssystem Auto GRASP zur modellbasierten, automatisierten Planung von Greifoperationen bekannter Objekte im Arbeitsraum eines Roboters. Im Gegensatz zu existierenden Greifplanungssystemen werden bei der Planung sämtliche erforderlichen Nebenbedingungen des Greifvorgangs berücksichtigt. Das vorgestellte Greifverfahren beruht auf einer effizienten Zweiteilung der Planung in eine Offline- und in eine Online-Phase. Während der Offline-Phase erfolgt eine maximale Modelldatenaufbereitung der zu greifenden Objekte. Geometrische Filteroperationen, die eine Art Shape-Matching zwischen der Geometrie des eingesetzten Parallelbackengreifers und der zu greifenden Objekte durchführen, generieren Griffklassen eines Objektes. Hierbei beschreibt eine Griffklasse eine Menge von Greifkoordinatensystemen, die für den Greifer unter konstanter Orientierung kollisionsfrei erreichbar sind. Als Orientierungen werden repräsentative Greiferorientierungen bestimmt, die zu Formschluß mit der Handbasis des Greifers und damit zu einer Erhöhung der Griffstabilität führen. Sämtliche generierten Griffklassen werden unter Berücksichtigung diverser geometrischer Kriterien bewertet, die aus den Ergebnissen der Modelldatenaufbereitung folgen. Daneben werden objektspezifische Merkmale bestimmt, die in die Online-Phase der Planung von Greifoperationen einfließen. Für die Planung evtl. erforderlicher Umgreifoperationen werden ebenfalls im Rahmen der Modelldatenaufbereitung Plazierungsklassen sämtlicher Objekte der Modellwelt generiert und evaluiert. Eine Plazierungsklasse eines Objektes beschreibt eine Menge von stabilen Plazierungen auf einer horizontalen Ablagefläche, die einen gemeinsamen Kontaktbereich besitzen. Zur Bewertung der Stabilität einer Plazierungsklasse wird eine anschaulich zu interpretierende Evaluierungsfunktion eingeführt. Die Ergebnisse der Modelldatenaufbereitung fließen in die Online-Phase der Planung von Greifoperationen ein.Grasping has evolved from a somewhat marginal topic to an important field in robotics research. This increasing interest in grasping is partly due to the increasing importance of flexible assembly in industrial automation. The thesis describes the model based grasp planning system Auto GRASP for automatically grasping objects in a robot’s workspace. In contrast to existing grasp planning systems various constraints are taken into account required for a successful execution of a grasp operation. The computations performed by Auto GRASP are split into offline and online computations, with as much a priori knowledge as possible used in the offline phase. During the offline phase a geometric grasp planning is performed using the concept of symbolic grasps. Symbolic grasps are generated by filter operations performing a kind of shape matching between the geometry of the gripper and the objects to be grasped. To reduce computational costs, representative gripper orientations are determined for each symbolic grasp. The new concept of representative gripper orientations guarantees, that the gripper’s palm can achieve form closure with the objects to be grasped. Thus, higher stability is achieved to resist dynamic disturbance forces arising during the motion of the robot. For each representative gripper orientation collision free approach trajectories and grasp frames are calculated in a local xy-configuration space respective to the objects. The resulting sets of grasp frames define grasp classes that are evaluated taking into account several evaluation criteria. For the generation of regrasp sequences, placement classes of objects are generated and evaluated. Placement classes describe stable object placements on a horizontal plane