• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.273-280
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• 2009

It is difficult to find a practical solution for the backward-motion control of a car-like mobile robot with n passive trailers. Unlike an omni-directional robot, a car-like mobile robot has nonholonomic constraints and limitations of the steering angle. For these reasons, the backward motion control problem of a car-like mobile robot with $n$ passive trailers is not trivial. In spite of difficulties, backing up a trailer system is useful for parking control. In this study, we proposed a mechanical alteration which is connecting $n$ passive trailers to the front bumper of a car to improve the backward motion control performance. Theoretical verification and simulations show that the backward-motion control of a general car with n passive trailers can be successfully carried out by using the proposed approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2315-2320
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• 2015

The robot has a smart function with the development of mobile devices and provides safety and convenience to our lives. In this paper, we study multiple robotcar system based on mobile that can be controlled by remote control. Robotcar with arduino can be controlled from server, but it also designed to be controlled by mobile devices as controller's necessary. Besides, it have feature that many robotcars can be controlled at the same time by using several mobile devices which select each robotcar.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.198-208
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• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.209-215
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• 2022

As intelligent autonomous driving vehicle development has become a big topic around the world, accurate reference dynamics estimation has been more important than before. Current systems generally use speed and heading information sensed from a distant point as a vehicle reference dynamic, however, the dynamics between different points are not same especially during rotating motions. In order to estimate properly estimate the reference dynamics from the information such as velocity and heading sensed at a point distant from the reference point such as center of gravity, this study proposes estimating reference dynamics from any location in the vehicle by combining the Bicycle and Ackermann models. A test system was constructed by implementing multiple GNSS/INS equipment on an Robot Operating System (ROS) and an actual car. Angle and speed errors of 10° and 0.2 m/s have been reduced to 0.2° and 0.06 m/s after applying the suggested method.