• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.726-730
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• 1990

In this paper, assuming that local range information is available, a collision-free path planning algorithm for multiple mobile robots is presented by using Hopfield neural optimization network. The energy function of the network is built using the present position and the goal position of each robot as well as its local range information. The proposed algorithm has several advantages such as the effective passing around obstacles with the directional safety distance, the easy implementation of robot motion planning including its rotation, the real-time path planning capability from the totally localized computations of path for each robot, and the adaptivity on arbitrary environment since any special shape of obstacles is not assumed.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2315-2317
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• 1998

There has been extensive efforts about robot path planning. Some major approaches are the roadmap approach, potential field approach and the cell decomposition approach. However, most of the path planning methods proposed so far based on above approaches consider the terrains filled with binary obstacles, i.e., if there exists an obstacle, robot simply cannot pass the location. In this paper, A mobile robot path planning method based on the cell decomposition technique for mobile robot that takes account of the terrain with varing degrees of travers-ability is discussed.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.127-130
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• 2017

In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1272-1277
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• 2014

This article describes a path planning algorithm for fixed-wing UAVs when a real terrain should be considered. Nowadays, many UAVs are required to perform mission flights near given terrain for surveillance, reconnaissance, and infiltration, as well as flight altitude of many UAVs are relatively lower than typical manned aerial vehicles. Therefore, real terrain should be considered in path planning algorithms of fixed-wing UAVs. In this research, we have extended a spline-$RRT^*$ algorithm to three-dimensional planner. The spline-$RRT^*$ algorithm is a $RRT^*$ based algorithm, and it takes spline method to extend the tree structure over the workspace to generate smooth paths without any post-processing. Direction continuity of the resulting path is guaranteed via this spline technique, and it is essential factor for the paths of fixed-wing UAVs. The proposed algorithm confirm collision check during the tree structure extension, so that generated path is both geometrically and dynamically feasible in addition to direction continuity. To decrease degrees of freedom of a random configuration, we designed a function assigning directions to nodes of the graph. As a result, it increases the execution speed of the algorithm efficiently. In order to investigate the performance of the proposed planning algorithm, several simulations are performed under real terrain environment. Simulation results show that this proposed algorithm can be utilized effectively to path planning applications considering real terrain.

• Journal of the HCI Society of Korea
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• v.5 no.2
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• pp.25-31
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• 2010

This paper proposes a hybrid tour path planning system for multiple visitors in a museum. The proposed path planning system merges individual user profiles into a group profile by exploiting the multiplicative utilization algorithm. It then generates a tour path for the users based on mixed initiative decision of the system and the involved visitors. It automatically selects visiting sites when group users have highly similar preferences while it asks users to select their appropriate visiting sites among available sites when their preferences are different. We developed the hybrid path planning system based on a tabletop display and evaluated it with four different exhibition settings and 11 participants. We found that the mixed decision of the system and users was useful in building a tour path for a group of visitors.

• 한국노년학
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• v.37 no.3
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• pp.567-582
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• 2017

The purpose of this study is to investigate the path model consisted of action planning, physical activity, and depression in order to obtain an enhanced understanding of their relationship and to support the aged with depression in community. In order to achieve this, precedent study was reviewed and the program with physical activity and action planning was executed. the data of a investigation of action planning, physical activity, and depression of the aged participated in the program which was consisted of physical activity with Action Planning was used and 116 cases were analyzed. The data analysis was done by descriptive statistics, correlation analysis, and path analysis. The results were as follows. First, the path model was accepted. Second, the direct path of action planning to physical activity was significant. Third, the direct path of both action planning and physical activity to depression were significant. Fourth, the effect size of action planning to depression was more than that of physical activity. Fifth, the indirect path of action planning to depression through physical activity was significant. On the basis of these results, this suggests a need to add action planning on national physical activity plan, establish the delivery system for physical activity program with action planning in mental health center in community, and applicate narrative approach skills for qualitative improvement of action planning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2401-2403
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• 2002

Given a certain target point, the mobile robot's navigation could be mainly considered about two areas, 'how fast and accurate' and 'how safe'. Such problems regarding the velocity and stability possess close relationship with the path in which the mobile robot navigates in. Thus, the system proposed in this research paper was constructed so the mobile robot can obtain the optimum path by utilizing the information according to the environmental map, based on the Global Path Planning. Also by inducing the Local Path Planning method, it was constructed so that the robots can avoid the obstacles, which were not shown in the environmental map on-line. Particularly, by fusing the Local and Global Path Planning together, it is possible for the robots to plan similar path. At the same time, the focus was on the materialization of effective mobile robot's navigation. It was made possible by utilizing the Fuzzy Logic Control. Also, the validity of the algorithm proposed was proven through the trial experiment.

• 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.