Part-mating Tasks Research Articles

Stability analysis and motion planning strategies with fuzzy coordinator and learning for mobile base robotic part macro/micro-assembly tasks

This paper addresses problems of a mobile base robot’s part assembly. This process can be broken down into two phases. First, a macro-assembly, bringing a part to an assembly hole or a receptacle (target) for a purpose of a part mating. For the macro-manipulation task, a stability analysis of a mobile base robot subject to disturbances, such as an external impact force and torque as well as a tipping movement, is discussed. The mobile robotic system is stabilized by balancing the system moment through a fuzzy coordinator. Simulations are performed by applying external forces and torque to the system and adding disturbances to the mobile base’s tipping movement. Second, a micro-assembly, mating a part with a target. For the micro-manipulation task, two learning methodologies are presented. First, a learning strategy to minimize the entropy, uncertainty, and eliminate unneeded events in the plan related to avoiding jamming is described. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part mating. Next, a fuzzy stochastic learning method, based on the probability of a fuzzy set and a modified distance metric, to update the probability of a plan composed of fuzzy events used for the part mating task is introduced. The degree of uncertainty associated with the fuzzy event of plan is used as an optimality criterion, or cost function, e.g. minimum Hamming distance, for a specific task execution. The above techniques are applicable to a wide range of mobile robotic tasks including pick and place operations, maneuvering around workspace, manufacturing, part mating, or complex assembly tasks.

Automation of the car battery lid assembly operation

Automation of an assembly operation in an automotive lead/acid battery production plant is described in the paper. The operation—assembly of the polypropylene lids on the battery containers—is automated using robot technology. During the process of lid assembly, it is necessary for two plate stack electrodes to slide into two hollow lead terminals in the lid. In the part mating task the sticking is possible to occur bringing the assembly process into the halt. To design an automatic assembly system the lid assembly operation is considered as a problem of multiple peg-in-hole insertions. The paper investigates the basic principles of a multiple part mating task with special regard to the lid assembly. The theoretical and experimental findings are presented enabling improved understanding of the lid assembly process. On this basis, the robot workcell is built and assembly strategy proposed. Presented are the design of the workcell peripheral devices and incorporation of the workcell into the existing automotive battery production line.

Modeling of part-mating strategies for automating assembly operations for robots

From the concept of designing for automation, a system modeled from simple part-mating is proposed for helping automate the fine motion control phase of assembly operations. The proposed system consists of three subsystems. The first subsystem is a part-mating model categorized from different mating geometric constraints of simple parts. The second subsystem is a coding system, named structured assembly coding system (SACS), that codes different part-mating tasks with different levels of geometrical constraints. From this coding system, specific part-mating types can be identified for different tasks of assembly operations. Conversely, constraint configuration for each part-mating task can also be generated from the proposed part-mating model. After constraint configurations are identified for an assembly operation, the third subsystem is to automate an assembly operation by deducing the compliance control strategies. The proposed system is demonstrated through a case study of pump assembly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

力覚情報を用いたはめあい作業の学習

This paper treats a practical method to generate assembly strategies applicable to part-mating tasks that are of particular interest. The difficulties in devising reliable assembly strategies results from various forms of uncertainty such as an imperfect knowledge of the parts being assembled and limitations of the devices performing the assembly.Our approach to cope with this problem is to have the robot learn the appropriate control response to measured force vectors, that is, the mapping relation between sensing data and corrective motion of robot, during task execution.In this paper, the mapping is acquired by using a learning algorithm and represented with a binary tree type database. Remarkable features of the proposed method are the use of a priori knowledge and accomplishment of the task with little human trouble.Experiments are carried out by taking acount of practical production facilities. It is shown by experimental results that an ideal mapping is acquired effectively by using the proposed method and the assembly task is carried out smoothly.