Combining Heuristics in Assembly Sequence Planning

Assembly Sequence Planning is tackled by modelling and solving a planning problem that considers the execution of the plan in a system with multiple assembly machines. The objective of the plan is the minimization of the total assembly time (makespan). To meet this objective, the model takes into account the durations and resources for the assembly tasks, the change of configuration in the machines, and the transportation of intermediate subassemblies between different workstations. In order to solve the problem, different heuristics has been defined from two relaxed model of it, one considering only the precedence constraints among tasks, and the other one considering only the use of shared resources. From these basic heuristics, other ones have been defined, combining both types of information from the problem, so that the refinement produces substantial improvements over the initial heuristics.Ministerio de Ciencia y TecnologíaDPI2003-07146-C02-0

A Pomset-Based Model for Estimating Workcells' Setups in Assembly Sequence Planning

This paper presents a model based on pomsets (partially ordered multisets) for estimating the minimum number of setups in the workcells in Assembly Sequence Planning. This problem is focused through the minimization of the makespan (total assembly time) in a multirobot system. The planning model considers, apart from the durations and resources needed for the assembly tasks, the delays due to the setups in the workcells. An A* algorithm is used to meet the optimal solution. It uses the And/Or graph for the product to assemble, that corresponds to a compressed representation of all feasible assembly plans. Two basic admissible heuristic functions can be defined from relaxed models of the problem, considering the precedence constraints and the use of resources separately. The pomset-based model presented in this paper takes into account the precedence constraints in order to obtain a better estimation for the second heuristic function, so that the performance of the algorithm could be improved

The Use of a Complexity Model to Facilitate in the Selection of a Fuel Cell Assembly Sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomics

The use of a complexity model to facilitate in the selection of a fuel cell assembly sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomi

Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores

To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with added stochastic noise to yield robustness scores--the success probabilities of planned assembly motions. As running a simulation for every assembly motion is impractical, we train a convolutional neural network to map assembly operations, given as an image pair of the subassemblies before and after they are mated, to a robustness score. The neural network prediction is used within a planner to quickly prune out motions that are not robust. We demonstrate this approach on two-handed planar assemblies, where the motions are one-step translations. Results suggest that the neural network can learn robustness to plan robust sequences an order of magnitude faster than physics simulation.Comment: Presented at the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

With the majority of mobile robot path planning methods being focused on obstacle avoidance, this paper, studies the problem of Navigation Among Movable Obstacles (NAMO) in an unknown environment, with static (i.e., that cannot be moved by a robot) and movable (i.e., that can be moved by a robot) objects. In particular, we focus on a specific instance of the NAMO problem in which the obstacles have to be moved to predefined storage zones. To tackle this problem, we propose an online planning algorithm that allows the robot to reach the desired goal position while detecting movable objects with the objective to push them towards storage zones to shorten the planned path. Moreover, we tackle the challenging problem where an obstacle might block the movability of another one, and thus, a combined displacement plan needs to be applied. To demonstrate the new algorithm's correctness and efficiency, we report experimental results on various challenging path planning scenarios. The presented method has significantly better time performance than the baseline, while also introducing multiple novel functionalities for the NAMO problem

Haptic-enabled virtual planning and assessment of product assembly

Purpose: This study aims to present a new haptic-enabled virtual assembly system for the automatic generation and objective assessment of assembly plans. The system is intended to be used as an assembly planning tool along the product development process. Design/methodology/approach: The generation of product assembly plans is based on the analysis of the assembly movements and operations performed by the user during the virtual assembly execution, and the objective assessment of product assembly is based on the definition and computation of new proposed assembly metrics. Findings: To evaluate the system, a case study corresponding to the assembly of a mechanical component is presented and analyzed. The results demonstrate that the proposed system is an effective tool to plan and evaluate different product assembly strategies in a more practical and objective approach than existing assembly planning methods. Research limitations/implications: Although the virtual assembly execution time is larger than the real assembly execution time, the assembly planning and evaluation results provided by the system are valid. However, the development of higher performance collision detection algorithms is needed to reduce the simulation time. Originality/value: The proposed virtual assembly system is able to not only simulate and automatically generate assembly plans but also objectively assess them from the virtual assembly task execution. The introduction and use of several assembly performance metrics to objectively evaluate assembly strategies in virtual assembly also represents a novel contribution

Survey on assembly sequencing: a combinatorial and geometrical perspective

A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.Peer ReviewedPostprint (author’s final draft