• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.445-449
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• 1989

Achievement of a straight line motion in the Cartesian space has a matter of great importance. Minimization of task execution time with linear interpolation in the joint space, accomplishing of a approximation of straight line motion in the Cartesian coordinate is considered as the prespecified task. Such determination yields minimum time joint-trajectory subject to input torque constraints. The applications of these results for joint-trajectory planning of a two-link manipulator with revolute joints are demonstrated by computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.845-853
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• 2012

We propose time-optimal cornering motion trajectory planning and control algorithms for differential wheeled mobile robot with motor actuating voltage constraint, under piecewise constant control input condition. For time-optimal cornering trajectory generation, 1) we considered mobile robot's dynamics including actuator motors, 2) divided the cornering trajectory into one liner section, followed by two cornering section with angular acceleration and deceleration, and finally one liner section, and 3) formulated an efficient trajectory generation algorithm satisfying the bang-bang control principle. Also we proposed an efficient trajectory control algorithm and implemented with an X-bot to prove the performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.123-126
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• 2003

This paper presents an speed reference trajectory planning methods for vibration suppression in a t-mass resonant system which has a flexible coupling between a load and a driving motor. Due to this flexibility, the system often suffers vibration especially when the motor is controlled for higher speed command. The steady state conditions are utilized to derive desired load speed trajectory which does not cause the torsional vibration. And the desired motor speed trajectory is synthesized base on the relation between load and motor speed. The simulation and experiment result suggest that the proposed method effectively suppress the vibration.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.74-80
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• 2006

This paper addresses the trajectory optimization of Unmanned Aerial Vehicles(UAVs) under multiple ground threats like enemy's anti-air radar sites. The power of radar signal reflected by the vehicle and the flight time are considered in the performance cost to be minimized. The bank angle is regarded as control input for a 1st-order lag vehicle, and input parameter optimization method based on Sequential Quadratic Programming (SQP) is used for trajectory optimization. The proposed path planning method provides more practical trajectories with enhanced survivability than those of Voronoi diagram method.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.56-64
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• 2013

We propose an efficient minimum-time cornering motion planning algorithms for differential-driven wheeled mobile robots with motor control input constraint, under piecewise constant control input sections. First, we established mobile robot's kinematics and dynamics including motors, divided the cornering trajectory for collision-free into one translational section, followed by one rotational section with angular acceleration, and finally the other rotational section with angular deceleration. We constructed an efficient motion planning algorithm satisfying the bang-bang principle. Various simulations and experiments reveal the performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.621-626
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• 1989

An efficient algorithm for planning near-optimum trajectory of manipulators is proposed. The algorithm is divided into two stages. The first one is the optimization of time trajectory with given spatial path. And the second one is the optimization of the spatial path itself. To consider the second problem, the manipulator dynamics is represented using the path parameter "s", then a differential equation corresponding to the dynamics is solved as two point boundary value problem. In this procedure, the gradient method is used to calculate improved input torques.t torques.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.753-761
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• 1987

Approximation of a Cartesian straight line motion with linear interpolation in the joint space has many desirable advantages and applications. But inappropriate determination of the corresponding subtravelling and transition times makes such joint-trajectories violate the input torque/force constraints. An approach that can overcome this difficult and yield the joint trajectories utilizing the allowable maximum input torque/force is established in this paper. The effectiveness of these results is demonstrated by using a three-joint revolute manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2237-2242
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• 2005

The original RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected states, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. It is generally known that the performance of RRTs can be improved depending on the selection of the metrics in choosing the nearest vertex and bias techniques in choosing random states. We designed a path planning algorithm based on the RRT method for a remote-controlled mobile robot. First, we considered a bias technique that is goal-biased Gaussian random distribution along the command directions. Secondly, we selected the metric based on a weighted Euclidean distance of random states and a weighted distance from the goal region. It can save the effort to explore the unnecessary regions and help the mobile robot to find a feasible trajectory as fast as possible. Finally, the constraints of the actuator should be considered to apply the algorithm to physical mobile robots, so we select control inputs distributed with commanded inputs and constrained by the maximum rate of input change instead of random inputs. Simulation results demonstrate that the proposed algorithm is significantly more efficient for planning than a basic RRT planner. It reduces the computational time needed to find a feasible trajectory and can be practically implemented in a remote-controlled mobile robot.