Task Planner for Simultaneous Fulfillment of Operational, Geometric and Uncertainty-Reduction Goals

Our ultimate goal in robot planning is to develop a planner which can create complete assembly plans given as input a high level description of assembly goals, geometric models of the components of the assembly, and a description of the capabilities of the work cell (including the robot and the sensory system). In this paper, we introduce SPAR, a planning system which reasons about high level operational goals, geometric goals and uncertainty-reduction goals in order to create assembly plans which consist of manipulations as well as sensory operations when appropriate. Operational planning is done using a nonlinear, constraint posting planner. Geometric planning is accomplished by constraining the execution of operations in the plan so that geometric goals are satisfied, or, if the geometric configuration of the world prevents this, by introducing new operations into the plan with the appropriate constraints. When the uncertainty in the world description exceeds that specified by the uncertainty-reduction goals, SPAR introduces either sensing operations or manipulations to reduce that uncertainty to acceptable levels. If SPAR cannot find a way to sufficiently reduce uncertainties, it does not abandon the plan. Instead, it augments the plan with sensing operations to be used to verify the execution of the action, and, when possible, posts possible error recovery plans, although at this point, the verification operations and recovery plans are predefined

A new approach to tolerance analysis

Journal ArticleTolerance analysis is seen as part of a more general problem, namely handling data with uncertainty. Uncertain geometric data arises when interpreting measured data, but also in solid modeling where floating point approximations are common, when representing design tolerances, or when dealing with limited manufacturing precision. The common question is whether parts with uncertain shape fulfill certain functional specification. The question is expressed as geometrical relationship between toleranced objects. Unfortunately, tolerance based relations are often inconsistent, unlike relations between exactly represented objects. In this paper we survey current tolerance representation and analysis methods. We then derive our method of intuitionistic tolerance handling from a method developed for robust solid modeling. A new representational framework is proposed, which serves as the basis for robust geometric modeling and tolerance analysis. We illustrate the framework with examples of assembly design

Set-based design of mechanical systems with design robustness integrated

This paper presents a method for parameter design of mechanical products based on a set-based approach. Set-based concurrent engineering emphasises on designing in a multi-stakeholder environment with concurrent involvement of the stakeholders in the design process. It also encourages flexibility in design through communication in terms of ranges instead of fixed point values and subsequent alternative solutions resulting from intersection of these ranges. These alternative solutions can then be refined and selected according to the designers’ preferences and clients’ needs. This paper presents a model and tools for integrated flexible design that take into account the manufacturing variations as well as the design objectives for finding inherently robust solutions using QCSP transformation through interval analysis. In order to demonstrate the approach, an example of design of rigid flange coupling with a variable number of bolts and a choice of bolts from ISO M standard has been resolved and demonstrated

A CASE STUDY INVESTIGATING RULE BASED DESIGN IN AN INDUSTRIAL SETTING

This thesis presents a case study on the implementation of a rule based design (RBD) process for an engineer-to-order (ETO) company. The time taken for programming and challenges associated with this process are documented in order to understand the benefits and limitations of RBD. These times are obtained while developing RBD programs for grid assemblies of bottle packaging machines that are designed and manufactured by Hartness International (HI). In this project, commercially available computer-aided design (CAD) and RBD software are integrated to capture the design and manufacturing knowledge used to automate the grid design process of HI. The stages involved in RBD automation are identified as CAD modeling, knowledge acquisition, capturing parameters, RBD programming, debugging, and testing, and production deployment. The stages and associated times in RBD program development process are recorded for eighteen different grid products. Empirical models are developed to predict development times of RBD program, specifically enabling HI to estimate their return on investment. The models are demonstrated for an additional grid product where the predicted time is compared to actual RBD program time, falling within 20% of each other. This builds confidence in the accuracy of the models. Modeling guidelines for preparing CAD models are also presented to help in RBD program development. An important observation from this case study is that a majority of the time is spent capturing information about product during the knowledge acquisition stage, where the programmer\u27s development of a RBD program is dependent upon the designer\u27s product knowledge. Finally, refining these models to include other factors such as time for building CAD models, programmers experience with the RBD software (learning curve), and finally extending these models to other product domains are identified possible areas of future work

Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model

Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor

Analysis of uncertainties and geometric tolerances in assemblies of parts

Computer models of the geometry of the real world have a tendency to assume that the shapes and positions of objects can be described exactly. However, real surfaces are subject to irregularities such as bumps and undulations and so do not have perfect, mathematically definable forms. Engineers recognise this fact and so assign tolerance specifications to their designs. This thesis develops a representation of geometric tolerance and uncertainty in assemblies of rigid parts. Geometric tolerances are defined by tolerance zones which are regions in which the real surface must lie. Parts in an assembly can slop about and so their positions are uncertain. Toleranced parts and assemblies of toleranced parts are represented by networks of tolerance zones and datums. Each arc in the network represents a relationship implied by the tolerance specification or by a contact between the parts. It is shown how all geometric constraints can be converted to an algebraic form. Useful results can be obtained from the network of tolerance zones and datums. For example it is possible to determine whether the parts of an assembly can be guaranteed to fit together. It is also possible to determine the maximum slop that could occur in the assembly assuming that the parts satisfy the tolerance specification. Two applications of this work are (1) tolerance checking during design and (2) analysis of uncertainty build-up in a robot assembly plan. I n the former, a designer could check a proposed tolerance specification to make sure that certain design requirements are satisfied. In the latter, knowledge of manufacturing tolerances of parts being manipulated can be used to determine the constraints on the positions of the parts when they are in contact with other parts