• 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 Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1029-1036
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• 2011

An energy-efficient reference walking trajectory generation algorithm is suggested utilizing allowable ZMP (Zero-Moment-Point) region, which maxmizes the energy efficiency for cyclic gaits, based on three-dimensional LIPM (Linear Inverted Pendulum Model) for biped robots. As observed in natural human walking, variable ZMP manipulation is suggested, in which ZMP moves within the allowable region to reduce the joint stress (i.e., rapid acceleration and deceleration of body), and hence to reduce the consumed energy. In addition, opimization of footstep planning is conducted to decide the optimal step-length and body height for a given forward mean velocity to minimize a suitable energy performance - amount of energy required to carry a unit weight a unit distance. In this planning, in order to ensure physically realizable walking trajectory, we also considered geometrical constraints, ZMP stability condition, friction constraint, and yawing moment constraint. Simulations are performed with a 12-DOF 3D biped robot model to verify the effectiveness of the proposed 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.

• 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 the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1471-1478
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• 1989

The optimal-time control of the coordinated motion of two robot manipulators may be of consequence in the industrial automation. In this paper two robot manipulators garsping a common object are assumed to travel a specified Cartesian path and the method how to derive the optimal-time solution is explained. This approach is based on parameterizing the corresponding patn and utilizing the phase-plame technique in the trajectory planning. Also the torques supplied by the actuators are assumed to have some constant bounds. The effectiveness of this approach is demonstrated by a computer simulation using a PUMA 560 manipulator.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.10
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• pp.1303-1309
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• 2014

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.526-528
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• 2004

This paper focuses on planning strategies for object interception, especially with minimum time. Herein, the goal is for robot to intercept object with minimum time on a conveyor line that flows to x-axis with respect to world coordinate system. In order to do it, conveyor system needs the algorithms for minimizing time. This objective is achieved by solving about two problems: selection of a minimum-time interception point and planning of an optimal robot trajectory. Herein, the first problem is formulated a minimization of the robot-object interception time.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.635-642
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• 1989

We formulated the multi-robot trajectory problem into a series of NLP problem, each of which is that of finding the optimal tip positions of the robots for the next time step. The NLP problem is composed of an objective function and three constraints, namely: a) Joint position limits, b) Joint velocity limits, and c) Collision-avoidance constraints. By solving a series of NLP problem, optimally coordinated trajectories can be determined without requiring any prior path information. This is a novel departure from the previous approach in which either all paths or at least one path is assumed to be given. Practical application of the developed method is for optimal synthesis of multiple robot trajectories in off-line. To test the validity and effectiveness of the method, numerical examples are illustrated.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.891-897
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• 2010

This paper proposes a cooperative control algorithm for a dual-arms robot which is carrying an object to the desired location. When the dual-arms robot is carrying an object from the start to the goal point, the optimal path in terms of safety, energy, and time needs to be selected among the numerous possible paths. In order to quantify the carrying efficiency of dual-arms, DAMM (Dual Arm Manipulability Measure) has been defined and applied for the decision of the optimal path. The DAMM is defined as the intersection of the manipulability ellipsoids of the dual-arms, while the manipulability measure indicates a relationship between the joint velocity and the Cartesian velocity for each arm. The cost function for achieving the optimal path is defined as the summation of the distance to the goal and inverse of this DAMM, which aims to generate the efficient motion to the goal. It is confirmed that the optimal path planning keeps higher manipulability through the short distance path by using computer simulation. To show the effectiveness of this cooperative control algorithm experimentally, a 5-DOF dual-arm robot with distributed controllers for synchronization control has been developed and used for the experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.53-60
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• 2010

We propose a motion planning method for autonomous mobile robots. In order to minimize traveling time, a smooth path and a time optimal velocity profile should be generated under kinematic and dynamic constraints. In this paper, we develop an effective and practical method to generate a good solution with lower computation time. The initial path is obtained from voronoi diagram by Dijkstra's algorithm. Then the path is improved by changing the graph and path simultaneously. We apply the dynamic programming algorithm into the stage of improvement. Simulation results are presented to verify the performance of the proposed method.