• 한국정밀공학회지
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• 제12권1호
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• pp.132-141
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• 1995

This paper presents a choosing method of turning devices for stable robotic assembly based on verification of a base assembly motion instability. In flexible assembly application, the base assembly needs to be maintained in its assembled state without being taken apart. Therefore, the instability of the base assembly motion should be considered when determining the guide line of choosing turning devices by evaluating a degree of the motion instability of the base assembly. To derive the instability, first we inference collision free assembly directions by extracting separable directions for the mating parts and calculate the separability which gives informations as to how the parts can be essily separated. Using these results, we determine the instability evaluated by summing all the modified separabilites of each component part within base assembly.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.841-847
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• 1999

For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential. Such informations can be acquired from visual sensors. For robotic assembly, the corrective assembly motion to compensate for such misalignments has to be determined from the measured informations. However, this may not be simply derived from the measured misalignment alone because the part deformation progressively occurs during misalignment compensation. Based on the analysis of flexible parts assembly process, this paper presents a neural net-based inference system that can infer the complex relationship between the corrective motion and the measured information of parts deformation and misalignments. And it verifies the performance of the implemented inference system. The results show that the proposed neural net-based misalignment compensation algorithm Is effective in compensating for the lateral misalignment, and that it can be extended to the assembly tasks under more general conditions.

• 제어로봇시스템학회논문지
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• 제1권2호
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• pp.112-118
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• 1995

In designing assembly lines, it is required that the lines should not only meet the demand of the product, but also minimize the assembly cost associated with the line. For such a purpose, numerous research efforts have been made on either the assembly sequence generation or the assembly line balancing. However, the works dealing with both the research problems have been seldom reported in literature. When assembly sequences are generated without consideration of line balancing, additional cost may be incurred, because the sequences may not guarantee the minimum number of workstations. Therefore, it is essential to consider line balancing in the generation of cost-effective assembly sequences. To incorporate the two research problems into one, this paper treats a single-model and deterministic (SMD) assembly line balancing (ALB) problem, and proposes a new method for generating line-balanced robotic assembly sequences by using a simulated annealing. In this method, an energy function is derived in consideration of the satisfaction of assembly constraints, and the minimization of both the assembly cost and the idle time. Then, the energy function is iteratively minimized and occasionally perturbed by the simulated annealing. When no further change in energy occurs, an assembly sequence with consideration of line balancing is finally found. To show the effectiveness of the proposed scheme, a case study for an electrical relay is presented.

• 대한기계학회논문집A
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• 제20권1호
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• pp.213-221
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• 1996

An assembly sequence is considered to be optimal when is minimizes assembly cost while satisfying assembly constraints. To derive such an optimal sequence for robotic assembly, this paper proposes a method using a simulated annealing algorithm. In this method, an energy funciton is derived inconsideration of both the assembly constraints and the assembly cost. The energy function thus derived is iteratively minimized until no further change in energy occurs. During the minimization, the energy is occationally perturbed probabilistically in order to escape from local minima. The minimized energy yields an optimal assembly sequence. To show the effectiveness of the proposed method, case studies are presented for industrial products such as an electrical relay and an automobil alternator. The performance is analyzed by comparing the results with those of a neural network-based method, based upon the optimal solutions of an expert system.

• 한국산업융합학회 논문집
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• 제25권6_1호
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• pp.953-959
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• 2022

In this paper, the design result of robotic tools and the development of robot control system for chemical coupler assembly are presented. This research aims to eliminate the risk of chemicals exposed to human operators by developing the robotic tools and robot automation system for chemical tank lorry unloading that were done manually. Due to tight tolerance between couplers, even small pose error may result in very large internal force. In order to resolve the problem, the 6-axis compliance device is employed, which can provide not only enough compliance between couplers but also F/T sensing. The 6-axis compliance device having large force and moment capacity is designed. A simple linear gripper with rack-and-pinion is designed to grasp two sizes of couplers. The proposed robot automation system consists of 6-DOF collaborative robot with offset wrist, 6-axis compliance device with F/T sensing, linear gripper, and two robot visions.

• 한국생산제조학회지
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• 제25권6호
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• pp.517-521
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• 2016

General industrial robots are difficult to use for precision assembly because they are operated based on position control. Their position accuracy is also usually higher than the assembly clearance (several tens of ${\mu}m$). In previous researches, force control was suggested as a robotic assembly solution. However, this method is difficult to apply in reality because of speed and cost problems. The RCC provides high speed, but applications are limited because the compliance is fixed, and it cannot detect an assembly condition during a task. A variable passive compliance device (VPCD) was developed herein. The VPCD can detect the assembly condition during tasks. This device can provide proper compliance for successful assembly tasks. The pneumatic system and the Stewart platform with an LVDT sensor were applied for measuring the displacement and variable compliance, respectively. The concept design and analysis were conducted to prove the effectiveness of the developed VPCD.

• 로봇학회논문지
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• 제18권3호
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• pp.352-357
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• 2023

Since robotic assembly in an unstructured environment is very difficult with existing control methods, studies using artificial intelligence such as reinforcement learning have been conducted. However, since long-time operation of a robot for learning in the real environment adversely affects the robot, so a method to shorten the learning time is needed. To this end, a method based on a pre-trained neural network was proposed in this study. This method showed a learning speed about 3 times than the existing methods, and the stability of reward during learning was also increased. Furthermore, it can generate a more optimal policy than not using a pre-trained neural network. Using the proposed reinforcement learning-based assembly trajectory generator, 100 attempts were made to assemble the power connector within a random error of 4.53 mm in width and 3.13 mm in length, resulting in 100 successes.

• 대한기계학회논문집
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• 제18권1호
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• pp.1-11
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• 1994

This paper presents an intelligent robotic control method for chamferless parts mating by integrating fuzzy control and neural network. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as not only the limitation of the devices performing the assembly but also imperfect knowledge of the parts being assembled. To cope with these problems, an intelligent robotic assembly method is proposed, which is composed of fuzzy controller and learning mechanism based upon neural net. In this method, fuzzy controller copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly scheme is evaluted through a series of experiments using SCARA robot. The results show that the proposed control method can be effectively applied to chamferless precision parts mating.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.49-54
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• 1992

A flexible robotic assembly cell is modeled using Petri net. A simulator is developed and used to predict the optimal status of the system. The assembly cell of flexible manufacturing system(FMS) pilot plant of Automation and Systems Research Institute(ASRI) in Seoul National University is modeled. The system consists of 3 robots, 4 conveyors, automatic guided vehicle(AGV) and auto-stacker. The simulator is programmed in Turbo C on IBM PC, supporting a simple graphic simulation with pull-down menu. The flexibility of the assembly cell in the FMS plant is guaranteed, since it is possible to predict the optimal status of the system using this simulator.

• 로봇학회논문지
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• 제10권1호
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• pp.42-51
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• 2015

Peg-in-hole assembly is the most representative task for a robot to perform under contact conditions. Various strategies for accomplishing the peg-in-hole task with a robot exist, but the existing strategies are not sufficiently practical to be used for various assembly tasks in a human environment because they require additional sensors or exclusive tools. In this paper, the peg-in-hole assembly experiment is performed with anthropomorphic hand arm robot without extra sensors or devices using "intuitive peg-in-hole strategy". From this work, the probability of applying the peg-in-hole strategy to a common assembly task is verified.