• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.551-556
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• 2014

This paper proposes a balancing control and driving control of a bicycle robot based on dynamic modeling of the bicycle robot, which has been derived using the Lagrange equations. For the balancing control of the bicycle robot, a reaction wheel pendulum method has been adopted in this research. By using the dynamics equations of the bicycle robot, an LQR controller has been designed for a balancing and driving control of a bicycle robot. The performance of the balance control is verified experimentally before the driving control, which shows a stable posture within one degree vibrations. To show the dynamic characteristics of the bicycle robot during driving, a trapezoidal velocity trajectory is selected as the references. Through simulations and real experiments, the effectiveness of the proposed algorithm has been demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.532-539
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• 2012

This paper proposes a balancing and driving control system for a bicycle robot. A reaction wheel pendulum control method is adopted to maintain the balance while the bicycle robot is driving. For the driving control, PID control algorithm with a variable gain adjustment has been developed in this paper, where the gains are heuristically adjusted during the experiments. To measure the angles of the wheels the encoders are used. For the balancing control, a roll controller is designed with a non-model based algorithm to make the shortest cycle. The tilt angle is measured by the fusion of the acceleration and gyroscope sensors, which is used to generate the control input of the roll controller to make the tilt angle zero. The performance of the designed control system has been verified through the real experiments with the developed bicycle robot.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1006-1013
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• 2011

In this paper, we propose an optimal posture control law for an unmanned bicycle by deriving linear bicycle model from fully nonlinear differential equations. We calculate each equilibrium point of a bicycle under any given turning radius and angular speed of rear wheel. There is only one equilibrium point when a bicycle goes straight, while there are a lot of equilibrium points in case of turning. We present an optimal equilibrium point which makes the leaning input minimum when a bicycle is turning. As human riders give rolling torque by moving center of gravity of a body, many previous studies use a movable mass to move center of gravity like humans do. Instead we propose a propeller as a new leaning input which generates rolling torque. The propeller thrust input makes bicycle model simpler and removes input magnitude constraint unlike a movable mass. The proposed controller can hold optimal equilibrium points using both steering input and leaning input. The simulation results on linear control for circular motion are demonstrated to show the validity of the proposed approach.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.292-297
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• 2013

This paper presents a new method of kinematic modeling for autonomous bicycle by using the differential motion transformation. Kinematic model is indispensable to trajectory planning and control for an autonomous mobile robot. The conventional methods of kinematic modeling for an autonomous bicycle depend on intuition by geometry. On the contrary, the proposed method in this paper is based on the systematic differential motion transformation, thus applicable to various types of autonomous bicycles. The differential motion transformation gives Jacobian between two coordinate frames and the velocity kinematics as a result.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.332-340
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• 2013

This paper reports a programming toolkit for implementing localization and navigation of a mobile robot both in real world and simulation. Many of the previous function libraries are difficult to use because of their complexity or lack of usability. The proposed toolkit consist of functions for dead reckoning, motion model, measurement model, and operations on directions or heading angles. The dead reckoning and motion model deals with differential drive robot and bicycle type robot driven by front wheel or rear wheel. The functions can be used for navigation in both real environment and simulation. To prove the feasibility of the toolkit, simulation results are shown along with the results in real environment. It is expected the proposed toolkit is used for test of algorithms for mobile robot navigation such as localization, map building, and obstacle avoidance.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.233-238
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• 2001

The mechanism design of the interactive emotional robot has been carried out. The two-wheeled inverted pendulum type mechanism was adopted to improve the mobility and make the innate clumsy monoaxial bicycle motion. Even though the system is unstable in itself, it is expected for the robot to move freely in a plane, keeping the upright position only with two wheels. Two motors attached on head can make 4 motion sets, and two motors on the wheels can make 8. Therefore, 32 independent motion sets can be achieved from the robot to communicate the emotions with humans. The motion's equation of the robot was derived based on nonholonomic dynamics, and the necessary power to the wheel's rotational axis was found by simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.315-316
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• 2013

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.161-167
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• 2006

In this paper, lateral force control of a mobile robot with slip is presented. First, the bicycle model of a mobile robot is derived for the front steering. Second, impedance force control algorithm is applied to regulate contact force with environment. The desired distance is specified conservatively inside the environment to guarantee to make contact. Different stiffness of environment has been tested for force tracking task. Simulation results show that the proposed control algorithm works well to maintain desired contact force on the environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.421-432
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• 2013

This paper describes implementation of functions for mobile robot localization, which is one of the vital technologies for autonomous navigation of a mobile robot. There are several function libraries for mobile robot navigation. Some of them have limited applicability for practical use since they can be used only for simulation. Our research focuses on development of functions which can be used for localization of indoor robots. The functions implement deadreckoning and motion model of mobile robots, measurement model of range sensors, and frequently used calculations on angular directions. The functions encompass various types of robots and sensors. Also, various types of uncertainties in robot motion and sensor measurements are implemented so that the user can select proper ones for their use. The functions are tested and verified through simulation and experiments.

• 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.