• Journal of the Korean Society of Safety
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• v.28 no.6
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• pp.49-56
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• 2013

The statistics of recent accidents in warehouses show that a heavy toll of lives were produced by various accidents, e.g. collision, overturn, fall, slip, exposure to harmful substances or environments, etc. Of significant concern amongst them is the collision, especially the collision between forklift and storage rack structure. Accordingly, this study focuses on behavioral characteristics of rack structure subjected to dynamic impact loading of a forklift. For this purpose, time-domain response analysis has been performed on a standard 2-bay six-story rack structure consisting of columns, beams and bracing members with perforated open section. In order to investigate the most critical scenario, the impact loads are applied in both down-aisle and cross-aisle directions, and the impact locations are also varied along the shelves of the palettes. In order to deal with storage distributions, three types of rack structures are further taken into account: original empty rack structure with no storage, half-loaded rack structure and fully-loaded rack structure. The numerical simulation results demonstrate that the dynamic characteristics of the rack structure are significantly dependent on the distribution of the storage goods and its natural period varies from 0.24sec to 1.06sec, approximately 4.4 times. Further, the parametric studies show that the forklift impact is most critical to the safety of the rack structure when it collides either at the base or at the top of the rack structure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1483-1490
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• 2009

This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.770-778
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• 2023

Purpose: In order to reduce forklift industrial accidents, it is necessary to classify them into institutional, technical, and educational factors and conduct research on whether each factor affects disaster reduction. Method: Descriptive statistical analysis, validity analysis, reliability analysis, and multiple regression analysis were conducted using SPSS 18 program based on an offline questionnaire based on a 5-point Likert scale. Result: As a result of multiple regression analysis, it was found that institutional, technical, and educational factors, which are independent variables for disaster reduction, explain about 62.5% of the variance in disaster prevention, which is the dependent variable. The regression model verification was found to be statistically significant with F=118.775 and significance probability p<0.01. Conclusion: First, there is a need to prevent disasters by including electric forklifts weighing less than 3 tons in the inspection system. Second, there is a need to make it mandatory to install front and rear cameras and forklift line beams to prevent forklift collision disasters. Third, there is a need to conduct special training related to forklifts every year, and drivers and nearby workers need to be included in the special training for forklifts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.229-231
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• 2018

As autonomous vehicle technology has been gradually developed, robots that have introduced autonomous navigation systems have been actively involved in areas where there is a lot of livelihoods such as industrial sites and accident sites. For this reason, the unmanned transportation system equipped with the autonomous traveling system is widely used in harmful environments where human access is difficult. In addition, the introduction of the autonomous driving system reduces the collision and casualties that occur in a mobility environment like the industrial field, and it helps the efficient work process. In addition, autonomous driving vehicles can be handled more safely and quickly in a wider area by transmitting the surrounding environment of each vehicle to a server connected to each autonomous driving vehicle and passing it through the main server. In this paper, by utilizing V2X communication for autonomous unmanned forklift system, it can increase industrial workload, reduce loss of life and damage to property through wide area forklifts.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.116-122
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• 2014

This study proposes a system reliability analysis of rack storage facilities subjected to forklift colliding events. The proposed system reliability analysis consists of two steps: the first step is to identify dominant failure modes that most contribute to the failure of the whole rack facilities, and the second step is to evaluate the system failure probability. In the first step, dominant failure modes are identified by using a simulation-based selective searching technique where the contribution of a failure mode to the system failure is roughly estimated based on the distance from the origin in the space of the random variables. In the second step, the multi-scale system reliability method is used to compute the system reliability where the first-order reliability method (FORM) is initially used to evaluate the component failure probability (failure probability of one member), and then the probabilities of the identified failure modes and their statistical dependence are evaluated, which is called as the lower-scale reliability analysis. Since the system failure probability is comprised of the probabilities of the failure modes, a higher-scale reliability analysis is performed again based on the results of the lower-scale analyses, and the system failure probability is finally evaluated. The illustrative example demonstrates the results of the system reliability analysis of the rack storage facilities subjected to forklift impact loadings. The numerical efficiency and accuracy of the approach are compared with the Monte Carlo simulations. The results show that the proposed two-step approach is able to provide accurate reliability assessment as well as significant saving of computational time. The results of the identified failure modes additionally let us know the most-critical members and their failure sequence under the complicated configuration of the member connections.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.344-352
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• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.