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KR-20260067001-A - Method and device for collision avoidance of unmanned vehicles and workers based on location information

KR20260067001AKR 20260067001 AKR20260067001 AKR 20260067001AKR-20260067001-A

Abstract

The present invention relates to a location-based safety management system for preventing collisions between unmanned vehicles and workers in industrial sites. In particular, it includes a technology that predicts the probability of collision in real time and provides phased warnings through an elliptical surveying method centered on the direction of travel of the unmanned vehicle and a weighted risk analysis based on the location, speed, and direction of movement of the worker and the vehicle. The present invention is expected to ensure worker safety and improve work efficiency by enabling accurate and rapid collision prevention in industrial sites.

Inventors

  • 이재용
  • 전대원
  • 유건수

Assignees

  • (주)휴빌론

Dates

Publication Date
20260512
Application Date
20241105

Claims (8)

  1. As a method for a location information-based collision avoidance device to prevent collisions between unmanned vehicles and workers in industrial sites, (a) A step of setting an elliptical area based on the current position and direction of travel of the unmanned vehicle; (b) a step of measuring the worker's position and monitoring the direction of movement; (c) a step of predicting the collision risk by applying weights according to the position, speed, and direction of travel of the worker and the unmanned vehicle within the elliptical area; and, (d) If the above collision risk level exceeds a certain standard, providing a warning notification according to the risk level reflecting the above weights A location information-based collision prevention method between an unmanned mobile vehicle and a worker, including
  2. In claim 1, The elliptical region setting of step (a) above is formed by making the direction of movement of the unmanned vehicle the major axis and the lateral direction the minor axis. A location information-based collision prevention method for unmanned vehicles and workers characterized by the following.
  3. In claim 1, Applying a higher weight to the major axis of the elliptical region according to the direction of movement of the unmanned mobile body A location information-based collision prevention method for unmanned vehicles and workers characterized by the following.
  4. In claim 1, Step (c) above predicts the possibility of a worker entering the elliptical area of the unmanned vehicle and provides a collision risk warning according to a risk level when the worker is located within the elliptical area. A location information-based collision prevention method for unmanned vehicles and workers characterized by the following.
  5. In claim 1, In step (d) above, when the operator approaches within a certain distance, the path of the unmanned vehicle is reset or an avoidance path is suggested to prevent a collision. A location information-based collision prevention method for unmanned vehicles and workers characterized by the following.
  6. In claim 1, The above step (a) includes the step of setting an elliptical measurement area based on GPS, Wi-Fi, and BLE signals according to the real-time location and direction of travel of the unmanned vehicle, and further includes the step of correcting the error of the elliptical measurement according to the indoor and outdoor location environment. A location information-based collision prevention method for unmanned vehicles and workers characterized by the following.
  7. A computer program stored on a non-transient storage medium for preventing collisions between unmanned mobile vehicles and workers based on location information, It is stored on a non-transient storage medium, and by a processor, (a) A step of setting an elliptical area based on the current position and direction of travel of the unmanned vehicle; (b) a step of measuring the worker's position and monitoring the direction of movement; (c) a step of predicting the collision risk by applying weights according to the position, speed, and direction of travel of the worker and the unmanned vehicle within the elliptical area; and, (d) If the above collision risk level exceeds a certain standard, providing a warning notification according to the risk level reflecting the above weights A computer program stored on a non-transient storage medium for performing a collision avoidance method between an unmanned mobile vehicle and a worker based on location information, which includes a command to cause to be executed.
  8. As a device for performing a location information-based collision prevention method between an unmanned mobile vehicle and a worker, (a) A step of setting an elliptical area based on the current position and direction of travel of the unmanned vehicle; (b) a step of measuring the worker's position and monitoring the direction of movement; (c) a step of predicting the collision risk by applying weights according to the position, speed, and direction of travel of the worker and the unmanned vehicle within the elliptical area; and, (d) If the above collision risk level exceeds a certain standard, providing a warning notification according to the risk level reflecting the above weights A location information-based collision avoidance device for unmanned vehicles and workers that enables the operation of

Description

Method and device for collision avoidance of unmanned vehicles and workers based on location information The present invention relates to safety management in industrial sites, and more specifically, to a location information-based method and device for preventing collisions between an unmanned vehicle and a worker, which predicts the likelihood of safety accidents based on location information of the worker and the unmanned vehicle and provides step-by-step collision warning lights in order to prevent collisions and accidents between the unmanned vehicle and the worker occurring at a work site. Recently, unmanned vehicles (e.g., autonomous robots, unmanned cranes, and automated transport vehicles) are being increasingly introduced in industrial settings to enhance automation and efficiency. These vehicles are significantly contributing to work automation, cost reduction, and productivity improvement across various sectors, including logistics, manufacturing, construction, and shipyards. However, the risk of collisions remains ever-present in work environments where unmanned vehicles and humans coexist. Such collisions can result in not only worker safety issues but also damage to production equipment and work stoppages, thereby highlighting the growing need for safety management and collision prevention systems in industrial settings. Most existing collision avoidance technologies primarily rely on providing warnings based on the straight-line distance between the operator's location and the moving object's location. For example, they attempt to prevent accidents by providing a warning notification to the operator or reducing the object's speed when it approaches within a certain distance. However, these conventional methods have limitations in predicting actual collision risks because they fail to account for the direction of travel of the unmanned vehicle and the operator's movement path. In particular, effective collision prevention is difficult with simple distance-based warnings in situations where unmanned vehicles are moving rapidly or in complex work environments. Therefore, for the safe operation of unmanned vehicles, a risk prediction system is required that comprehensively considers not only the distance from the operator but also the vehicle's direction of travel, speed, and the operator's direction of movement. For example, if an operator is located in a specific direction while the vehicle is moving rapidly, the risk becomes very high. Conversely, if the operator is in the opposite direction to the vehicle's movement, the risk may be relatively lower even if they are within the same distance. Thus, to more accurately assess collision risk, risk prediction that applies weights based on the direction of travel and speed is necessary, rather than simply providing warnings based on the distance between the vehicle and the operator. Furthermore, existing positioning technologies primarily utilize GPS, RFID, or BLE to track the location of moving objects or workers in real time. While these systems enable location tracking over a relatively wide range, they may have limitations in accurate location prediction. For example, although GPS performs well outdoors, its accuracy can decrease in indoor environments due to unstable signals. Similarly, while BLE and Wi-Fi-based positioning systems can be used indoors, their accuracy may decline in enclosed spaces or environments with many obstacles. As such, existing positioning technologies alone make it difficult to effectively manage the risk of collision between unmanned vehicles and workers in the complex environments of industrial sites. To address this, a hybrid positioning system must be introduced to enable precise location tracking both indoors and outdoors by utilizing various positioning technologies (GPS, Wi-Fi, BLE, etc.). Through this, location information must be accurately collected to suit the specific characteristics of industrial sites, and a collision avoidance system based on this data must be provided. FIG. 1 is a flowchart illustrating a method for preventing collisions between an unmanned mobile vehicle and a worker based on location information according to the present invention. FIG. 2 is a diagram showing the configuration of a computer device equipped with a location information-based collision prevention application for an unmanned mobile vehicle and a worker according to the present invention. FIG. 3 is a diagram showing the configuration of a composite positioning system in a location information-based collision avoidance system for an unmanned mobile vehicle and a worker according to the present invention. FIG. 4 is a module configuration diagram showing a composite tag of a positioning collection device in a composite positioning system according to FIG. 3. Figure 5 is a sequence flow showing the distance measurement process of FTM. Figure 6 is a diagram showing a position estimation technique using trilateration. Figure 7 i