• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.3
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• pp.251-260
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• 2009

Hybrid locomotive UGV with actively articulated legs along with wheeled ends has high traversability to travel over rough terrain. The behavior control method was usually adapted for the controlling of the suspension configuration which determines the traversability of the UGV. In this study, we are proposing a method of configuration planning of the legs without any detail geometric data about the terrain. The terrain was estimated by the traces of each wheel and the leg configurations for the desired posture of the vehicle were set up against the constraints of the terrain. Also, an optimal leg configuration was calculated based on the quasi-static stability and power consumption, and plans for the leg behavior were made. Validity of the proposed method was checked by simulations using some off-the-shelf programs, and showed that the orientation control without geometric features of terrains and simplification of the behavior planning for obstacle negotiation were possible.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.116-128
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• 2013

This paper focuses on development of a testbed for analysis of robot-terrain interaction on rough terrain and also, through one wheel driving experiments using this testbed, prediction of maximum velocity and acceleration of UGV. Firstly, from the review regarding previous researches for terrain modeling, the main variables for measurement are determined. A testbed is developed to measure main variables related to robot-terrain interaction. Experiments are performed on three kinds of rough terrains (grass, gravel, and sand) and traction-slip curves are obtained using the data of the drawbar pull and slip ratio. Traction-slip curves are used to predict driving performance of UGV on rough terrain. Maximum velocity and acceleration of UGVs are predicted by the simple kinematics and dynamics model of two kinds of 4-wheel mobile robots. And also, driving efficiency of UGVs is predicted to reduce energy consumption while traversing rough terrains.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.557-564
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• 2012

This paper presents UGV localization using multi-sensor fusion based on federated filter in outdoor environments. The conventional GPS/INS integrated system does not guarantee the robustness of localization because GPS is vulnerable to external disturbances. In many environments, however, vision system is very efficient because there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper uses the scene matching and pose estimation based vision navigation, magnetic compass and odometer to cope with the GPS-denied environments. NR-mode federated filter is used for system safety. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.471-478
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• 2011

This UGV (Unmanned Ground Vehicle) is not only widely used in various practical applications but is also currently being researched in many disciplines. In particular, obstacle avoidance is considered one of the most important technologies in the navigation of an unmanned vehicle. In this paper, we introduce a simple algorithm for path planning in order to reach a destination while avoiding a polygonal-shaped static obstacle. To effectively avoid such an obstacle, a path planned near the obstacle is much shorter than a path planned far from the obstacle, on the condition that both paths guarantee that the robot will not collide with the obstacle. So, to generate a path near the obstacle, we have developed an algorithm that combines an edge detection method and a limit-cycle navigation method. The edge detection method, based on Hough Transform and IR sensors, finds an obstacle's edge, and the limit-cycle navigation method generates a path that is smooth enough to reach a detected obstacle's edge. And we proposed novel algorithm to solve local minima using the virtual wall in the local vision. Finally, we verify performances of the proposed algorithm through simulations and experiments.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.1-8
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• 2012

Obstacle detection is much studied by using sensors such as laser, vision, radar and ultrasonic in path planning for UGV(Unmanned Ground Vehicle), but not much reported about its characterization. In this paper not only an obstacle classification method using 2-dimensional LMS(Laser Measurement System) but also a decision making method whether to avoid or traverse the obstacle is proposed. The basic idea of decision making is to classify the characteristics by 2D laser scanned data and intensity data. Roughness features are obtained by range data using a simple linear regression model. The standard deviations of roughness and intensity data are used as measures for decision making by comparing with those of reference data. The obstacle classification and decision making for the UGV can facilitate a short path to the target position and the survivability of the robot.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.27-35
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• 2008

A navigation system is one of the important components of an unmanned ground vehicle (UGV). A GPS receiver collects data signals transmitted by (Earth orbiting) satellites. However, these data signals may contain many errors resulting misinformation and depending on one's position (environment), reception may be impossible. The proposed self-driven algorithm uses three low-cost GPS in order to minimize errors of existing inexpensive single GPS's driving algorithm. By using reliable final data, which is analyzed and combined from each of three GPS's received data signals, gathering a vehicle's steering performance information and its current pin-point position is improved even with error containing signals or from a place where signal gathering is impossible. The purpose of this thesis is to explain navigation system algorithm using multiple GPS and compass sensor and prove the algorithm through experiments.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.735-738
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• 2008

A 6 D.O.F Stewart platform type parallel robot has been developed as a simulator to test the surveillance robot stabilization control. Since the surveillance robot is installed on the unmanned ground vehicle (UGV), it is required to have a stabilization control system to compensate the disturbance from the UGV. PID control scheme has been applied to the parallel robot to generate controlled motion following the input motion.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.5-13
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• 2005

The use of robot is no longer limited in the industrial scene, and becoming expanded toward many aspects of human life. Especially, military robot closely concerned with our lives seems to advance more and more in the future. As a need analysis for developing military robot, this project conducted a poll about Unmanned Reconnaissance Robot, and on the basis of the result, I suggested 3 directions of developing UGV(Unmanned Ground Vehicle) suitable to strategic environment of Korea.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.404-414
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• 2016

Autonomy of unmanned systems is important because the unmanned system with high level of autonomy is able to perform desired tasks in unstructured environments without continuous human guidance. Evaluation of their autonomy is vital to realize the autonomous operation ability of unmanned system. Compared to the methods of evaluating the level of autonomy(LOA) for an unmanned ground vehicle(UGV) and unmanned aerial vehicle(UAV), the method of expressing the LOA of unmanned maritime system(UMS) is not established yet. Since UMS has a unique characteristics in terms of operational area, mission complexity and required technologies, compared to the UGV and UAV, it is required to establish for expressing the LOA for UMS. This paper reviews the current approaches to assess the LOA of unmanned system and proposes potential metrics for UMS in order to determine the autonomy levels of UMS.