• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1419-1423
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• 2004

In this paper, we propose an algorithm for planning an optimal path to capture a moving object by a mobile robot in real-time. The direction and rotational angular velocity of the moving object are estimated using the Kalman filter, a state estimator. It is demonstrated that the moving object is tracked by using a 2-DOF active camera mounted on the mobile robot and then captured by a mobile manipulator. The optimal path to capture the moving object is dependent on the initial conditions of the mobile robot, and the real-time planning of the robot trajectory is definitely required for the successful capturing of the moving object. Therefore the algorithm that determines the optimal path to capture a moving object depending on the initial conditions of the mobile robot and the conditions of a moving object is proposed in this paper. For real-time implementation, the optimal representative blocks have been utilized for the experiments to show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.186-190
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• 2003

In this paper, we propose an algorithm for planning an optimal path to capture a moving object by a mobile robot in real-time. The direction and rotational angular velocity of the moving object are estimated using the Kalman filter, a state estimator. It is demonstrated that the moving object is tracked by using a 2-DOF active camera mounted on the mobile robot and then captured by a mobile manipulator. The optimal path to capture the moving object is dependent on the initial conditions of the mobile robot, and the real-time planning of the robot trajectory is definitely required for the successful capturing of the moving object. Therefore the algorithm that determines the optimal path to capture a moving object depending on the initial conditions of the mobile robot and the conditions of a moving object is proposed in this paper. For real-time implementation, the optimal representative blocks have been utilized for the experiments to show the effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1193-1200
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• 2015

An effective swing trajectory of legged robots is different from the swing trajectories of humans or animals because of different dynamic characteristics. Therefore, it is important to find optimal parameters through experiments. This paper proposes a real-time nonlinear programming (RTNLP) method for optimization of the swing trajectory of the legged robot. For parameterization of the trajectory, the swing trajectory is approximated to parabolic and cubic spline curves. The robotic leg is position-controlled by a high-gain controller, and a cost function is selected such that the sum of the motor inputs and tracking errors at each joint is minimized. A simplified dynamic model is used to simulate the dynamics of a robotic leg. The purpose of the simulation is to find the feasibility of the optimization problem before an actual experiment occurs. Finally, an experiment is carried out on a real robotic leg with two degrees of freedom. For both the simulation and the experiment, the design variables converge to a feasible point, reducing the cost value.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.405-413
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• 2006

A novel approach based on genetic algorithms (GA) is developed to find a global minimum-jerk trajectory of a space robotic manipulator in joint space. The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance of minimizing the jerk is to reduce the vibrations of manipulator. In this formulation, a global genetic-approach determines the trajectory by minimizing the maximum jerk in joint space. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use an genetic algorithm to search the optimal joint inter-knot parameters in order to realize the minimum jerk. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation result shows that GA-based minimum-jerk trajectory planning method has satisfactory performance and real significance in engineering.

• Journal of Power Electronics
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• v.16 no.2
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• pp.584-597
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• 2016

This paper focuses on an improvement in the transient performance of Boost converters when the load changes abruptly. This is achieved on the basis of the nature trajectory in Boost converters. Three key aspects of the transient performance are analyzed including the storage energy change law in the inductors and capacitors of converters during the transient process, the ideal minimum voltage deviation in the transient process, and the minimum voltage deviation control trajectory. The changing relationship curve between the voltage deviation and the recovery time is depicted through analysis and simulations when the load suddenly increases. In addition, the relationship curve between the current fluctuation and the recovery time is obtained when the load suddenly decreases. Considering the aspects of an increasing and decreasing load, this paper proposes the transient performance synthetic optimized trajectory and control laws. Through simulation and experimental results, the transient performances are compared with the other typical three control methods, and the ability of proposed synthetic trajectory and control law to achieve optimal transient performance is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3150-3170
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• 2016

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.223-225
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• 2006

A ball moving on a beam is a typical nonlnear dynamic system, which is often adopted to proof test diverse control schemes. Ball and plate system is the extension of the traditional ball and beam problem that moves a metal ball on a rigid plate. In this paper, a trajectory planning and tracking problem is proposed for ball and plate system, which is to control the ball from a point to another without hitting the obstacles. Our scheme is composed of three controllers, TS type optimal path tracking controller, mandani type obstacle avoidance controller and trajectory planning controller that determines the desired trajectory. But this type of construction can give rise to chattering executions. Because the difference of contributions from concurrent controllers can cause behaviors unsmoothly. We propose fuzzy pid supervision control1er to handle this problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.206-214
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• 2003

In this paper, we propose a method to avoid obstacles that have unstable limit cycles in a chaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. When a chaos robot meets an obstacle in an Arnold equation or Chua's equation trajectory, the obstacle reflects the robot. We also show computer simulation results of Arnold equation and Chua's equation and random walk chaos trajectories with one or more Van der Pol obstacles and compare the coverage rates of each trajectory. We show that the Chua's equation is slightly more efficient in coverage rates when two robots are used, and the optimal number of robots in either the Arnold equation or the Chua's equation is also examined.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.206-213
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• 2002

This paper proposes a vehicle trajectory prediction method for application to vehicle-to-vehicle distance control. This method is based on 2-dimensional kinematics and a Kalman filter has been used to estimate acceleration of the object vehicle. The simulation results using the proposed control method show that the relative distance characteristics can be improved via the trajectory prediction method compared to the customary vehicle stop and go cruise control systems which makes the vehicle remain at a safe distance from a preceding vehicle according to the driver's preference, automatically slow down and come to a full stop behind a preceding vehicle.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.58-67
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• 2022

In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.