• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.301-305
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• 2001

Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking(or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, complex, coupled manipulator dynamics. The minimum-time manipulator control problem is solved for the case when the path is specified and the actuator torque limitations are known. In path planning, DP is applied to applied to find the shortest path form initial position to final position with the assumptions that there is no obstacle and that each path is straight line. In path control, the phase plane technique is applied to the minimum-time control with the assumptions that the bound on each actuator torque is a function of joint position and velocity or constant. This algorithm can be used for any manipulator that has rigid link, known dynamics equations of motion, and joint angles that can be determined at a given position on the path.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.274-277
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• 2000

In this paper we propose new algorithms of path planning and obstacle avoidance for an autonomous mobile robot with vision system. Distance variation is included in path planning to approach the target point and avoid obstacles well. The fuzzy rules are also applied to both trajectory planning and obstacle avoidance to improve the autonomy of mobile robot. It is shown by computer simulation that the proposed algorithm is working well.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.423-427
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• 1993

A velocity planning method is proposed that ensures collision-free and minimal delay-time motions for two robotic manipulators and auxiliary equipments. Additional path, which makes robot start with possible largest speed, is added to the original prescribed path of one of two robots, and this running start along the additional path reduces delay time introduced to avoid collision between the robots and therefore reduces total traveling time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.265-276
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• 2017

In this paper, a robust and more accurate trajectory tracking control method for a mobile robot is proposed using WIPDC(Weighted Integral Parallel Distributed Compensation) and T-S Fuzzy disturbance observer. WIPDC reduces the steady state error by adding weighted integral term to PDC. And, T-S Fuzzy disturbance observer makes it possible to estimate and cancel disturbances for a T-S fuzzy model system. As a result, the trajectory tracking controller based on T-S Fuzzy disturbance observer shows robust tracking performance. When the initial postures of a mobile robot and the reference trajectory are different, the initial control inputs to the mobile robot become too large to apply them practically. In this study, also, the problem is solved by designing an initial approach path using a path planning method which employs $B\acute{e}zier$ curve with acceleration limits. Performances of the proposed method are proved from the simulation results.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.3
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• pp.281-287
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• 2023

Establishing a ship's passage plan is an essential step before it starts to sail. The research related to the automatic generation of ship passage plans is attracting attention because of the development of maritime autonomous surface ships. In coastal water navigation, the land, islands, and navigation rules need to be considered. From the path planning algorithm's perspective, a ship's passage planning is a global path-planning problem. Because conventional global path-planning methods such as Dijkstra and A^* are time-consuming owing to the processes such as environmental modeling, it is difficult to modify a ship's passage plan during a voyage. Therefore, the D^* algorithm was used to address these problems. The starting point was near Busan New Port, and the destination was Ulsan Port. The navigable area was designated based on a combination of the ship trajectory data and grid in the target area. The initial path plan generated using the D^* algorithm was analyzed with 33 waypoints and a total distance of 113.946 km. The final path plan was simplified using the Douglas-Peucker algorithm. It was analyzed with a total distance of 110.156 km and 10 waypoints. This is approximately 3.05% less than the total distance of the initial passage plan of the ship. This study demonstrated the feasibility of automatically generating a path plan in coastal navigation for maritime autonomous surface ships using the D^* algorithm. Using the shortest distance-based path planning algorithm, the ship's fuel consumption and sailing time can be minimized.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.34-41
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• 2003

A method is presented for generating the path which significantly reduces residual vibration of a flexible robot manipulator and applying control theory to track the desired path. The desired path is optimally designed so that the system completes the required move with minimum residual vibration. A closed loop control theory is applied to track the planned path in the case of load variation. Specifically, it is desired that the optimally designed path has a better trajectory tracking capabilities during the residual vibration over the cycloidal path, in various cases of load. Perturbation adaptive control is used as closed loop control scheme. A planar two link manipulator is used to evaluate this method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2417-2419
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• 2001

A path planning is one of the main subjects in a mobile robot. It is divided into two parts. One is a global path planning and another is a local path planning. This paper, using the formal two methods, presents that the mobile robot moves to multi-targets with avoiding unknown obstacles. For the shortest time and the lowest cost, the mobile robot has to find a optimal path between targets. To find a optimal global path, we used GA(Genetic Algorithm) that has advantage of optimization. After finding the global path, the mobile robot has to move toward targets without a collision. FLC(Fuzzy Logic Controller) is used for local path planning. FLC decides where and how faster the mobile robot moves. The validity of the study that searches the shortest global path using GA in multi targets and moves to targets without a collision using FLC, is verified by simulations.

• Journal of Oral Medicine and Pain
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• v.15 no.1
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• pp.79-89
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• 1991

The author examined the patterns and various ranges of mandibular movements in TMJ lock closed patients in the frontal, sagittal and horizontal plane and obtained the following results. 1. In the frontal trajectory, the mean amount of maximum mouth opening was 24.4mm and the opening paths were deviated to the affected side in 87.1% of the patients. The mean amount of maximum laterotrusion to the affected side was 10.4mm and that of non-affected side was 7.5mm. There was a significant difference between them(p<0.001). 2. In the sagittal trajectory, the mean amount of the maximum protrusion was 7.0mm, the mean amount of the maximal retrusion was 1.0mm 3. In the horizontal trajectory, the pattern of laterotrusion showed asymmetry: the mean length of non-affected side was smaller than that of the affected side. Protrusive path were deviated to the affected side in 64.5% of the patients, the mean degree of deviation was 16.4$^{\circ}$. The mandibular movements of TMJ lock-closed patients can be characterized by decreased range of mouth opening, protrusive movement, and laterotrusive movement to the non-affected side and also characterized by deviated opening and protrusive path to the affected side.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.789-795
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• 2011

In elliptical vibration cutting (EVC), cutting performance is largely affected by the shape of an elliptical path of the cutting tool. In this study, two parallel piezoelectric actuators were used to make an elliptical vibration cutting device. When harmonic voltages of $90^{\circ}$ out-of-phase are supplied to the EVC device, creation of an ideal elliptical trajectory whose major and minor axes are parallel to the cutting and thrust directions is anticipated from a kinematic analysis of the EVC device, however, the paths we experimentally observed showed significant distortions in its shape ranging from skew to excessive elongation of the major axis of the ellipse. To compensate distortions, an analytical model describing the elliptical path of the cutting tool was developed and verified with experimental results, and based on the analytical model, the distorted elliptical paths created at 100 Hz, 1 kHz, and 16 kHz were corrected for skew and elongation.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.