• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.6
• /
• pp.122-127
• /
• 1996

The achievement of the optimal condition for the task of an industrial articulated robot used in many fields is an important problem to improve productivity. In this paper, a minimum-time trajectory for an articulated robot along the specified path is studied and simulated with a proper example. A general dynamic model of manipulator is represented as a function of path distance. Using this model, the velocity is produced as fast as possible at each point along the path. This minimum-time trajectory planning module together with the existing collision-free path planning modules is utilized to design the optimal path planning of robot in cases where obstacles present.

• Journal of IKEEE
• /
• v.13 no.2
• /
• pp.150-158
• /
• 2009

For the autonomous movement, the optimal path planning connecting between current and target positions is essential, and the optimal path of mobile robot means obstacle-free and the shortest length path to a target position. Many actual mobile robots should move without any information of surrounded obstacles. Thus, this paper suggests new methods of path planning and obstacle avoidment, suitable in unknown environments. This method of path planning always tracks the local target expected as the optimal one, and the result of continuous tracking becomes the first generated moving path. This path, however, do not regard the collision with obstacles. Thus, this paper suggests a new method of obstacle avoidance resembled with the Potential Field method. Finally, a simulation confirms the performance and correctness of the path planning and obstacle avoidance, suggested in this paper.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.2
• /
• pp.195-201
• /
• 2010

Recently, the necessity of the autonomous mobile robot is emphasized in order to enlarge the scope of activity and actively cope with the change of work environment. This paper proposes the algorithm which enables the mobile robot to avoid obstacles and lead it to the destination by the shortest path. And we verify the usability by a simulation. We made the algorithm with micro-GA and $\lambda$-geometry MRA. The area of simulation is limited to 320(width)$\times$200(length) pixels and one pixel is one centimeter. When we planned the path with only $\lambda$-geometry MRA, we could find the direction for path planning but could not find the shortest path. But when we planned the path with $\lambda$-geometry MRA and micro-GA, we could find the shortest path. So the algorithm enables us to find the direction for path planning and the shortest path.

• Journal of Aerospace System Engineering
• /
• v.18 no.2
• /
• pp.56-63
• /
• 2024

As the safety of unmanned vehicles continues to improve, their usage in urban environments, which are full of obstacles such as buildings, is expected to increase. When numerous unmanned vehicles are operated in such environments, an algorithm that takes into account mutual collision avoidance, as well as static and dynamic obstacle avoidance, is necessary. In this paper, we propose an algorithm that handles task assignment and path planning. To efficiently plan paths, we construct a grid-based map and derive the paths from it. To enable quick re-planning in dynamic environments, we focus on reducing computational time. Through simulation, we explain obstacle avoidance and mutual collision avoidance in small-scale problems and confirm their performance by observing the entire mission completion time (Makespan) in large-scale problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.5
• /
• pp.103-111
• /
• 2021

The purpose of this study is to study a method for preventing collisions of multiple drones in controlled airspace. As a result of the study, it was proved that it is appropriate as a method to control drone collisions after setting accurate information on the ROI (Region of Interest) area estimated based on the expected drone path and time in the control system as a method to avoid drone collision. As a result of the empirical analysis, the diameter of the flight path of the operating drone should be selected to reduce the risk of collision, and the change in the departure time and operating speed of the operating drone did not act as an influencing factor in the collision. In addition, it has been demonstrated that providing flight priority is one of the appropriate methods as a countermeasure to avoid collisions. For collision avoidance methods, not only drone sensor-based collision avoidance, but also collision avoidance can be doubled by monitoring and predicting collisions in the control system and performing real-time control. This study is meaningful in that it provided an idea for a method for preventing collisions of multiple drones in controlled airspace and conducted practical tests. This helps to solve the problem of collisions that occur when multiple drones of different types are operating based on the control system. This study will contribute to the development of related industries by preventing accidents caused by drone collisions and providing a safe drone operation environment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.4
• /
• pp.498-504
• /
• 2005

A fuzzy logic for collision avoidance of underwater robots is proposed in this paper. The VFF(Virtual Force Field) method, which is widely used in the field of mobile robots, is modified for application to the autonomous navigation of underwater robots. This Modified Virtual Force Field(MVFF) method using the fuzzy logic can be used in either track keeping or obstacle avoidance. Fuzzy logics are devised to handle various situations which can be faced during autonomous navigation of underwater robots. The obstacle avoidance algorithm has the ability to handle multiple static obstacles. Results of simulation show that the proposed method can be efficiently applied to obstacle avoidance of the underwater robots.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.10
• /
• pp.1479-1485
• /
• 2015

Robot's technology was very simple and repetitive in the past. Nowadays, robots are required to perform intelligent operation. So, path planning has been studied extensively to create a path from start position to the goal position. In this paper, potential field algorithm was used for path planning in dynamic environments. It is used for a path plan of mobile robot because it is elegant mathematical analysis and simplicity. However, there are some problems. The problems are collision risk, avoidance path, time attrition. In order to resolve path problems, we amalgamated potential field algorithm with the artificial neural network system. The input of the neural network system is set using relative velocity and location between the robot and the obstacle. The output of the neural network system is used for the weighting factor of the repulsive potential function. The potential field algorithm problem of mobile robot's path planning can be improved by using artificial neural network system. The suggested algorithm was verified by simulations in various dynamic environments.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.7 no.1
• /
• pp.87-99
• /
• 2015

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle $A^*$ algorithm ($FAA^*$), an advanced $A^*$ algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.890-903
• /
• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.04a
• /
• pp.880-885
• /
• 1995

This paper deals with the efficient planning of robot motions in the Cartesian space while avoiding the collision with obstacles. The motion planning problem is to find a path from the specified starting robot configuration that avoids collision with a known set of stationary obstacles. A simple and efficient algorithm was developed using "Backward" approach to solve this problem. The computational result was satisfactory enough to real problems. problems.