• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.37.2-37
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• 2002

1. Introduction 2. Problem Formulation 3. Avoidability Measure And Relative Distance 4. Artificial Potential Field in Terms of Relative Distance 5. Simulation Results 6. Conclusions 7. Reference

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.913-917
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• 1994

A collision-free geometric path for industrial articulated robot is searched among polyhedral obstacles considering kinematic charcteristics. Then minimum-time control of the geometric path is studied considering dynamic characteristics. The algorithm is simulated on PC for maximum speed, moving time and so forth.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.328-333
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• 1994

This paper presents a real-time calculation method to generate the trajectory of robot manipulator for the purpose of avoiding collision. In order to model 3-D workspace, we use octree which has been used for fast collision detection. The levels of octree are used as the cost function to represent the distance between the manipulator and the obstacles. This criterion is not exact, but, due to this, we can obtain the approximate feasible trajectory extremely quickly. We will show the effectiveness of our method with some simulation examples. For example, the proposed method can solve a problem within 1 second on Intel 80486 processor running at 33 MHz. It has taken more than half an hour with one of the previously proposed methods.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.639-654
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• 2011

In this paper, we present a channel access mechanism, referred to as the enhanced mesh coordinated channel access (eMCCA) mechanism, for IEEE 802.11s-based wireless mesh networks. The current draft of IEEE 802.11s includes an optional medium access control (MAC), denoted as MCCA, which is designed to provide collision-free and guaranteed channel access during reserved periods. However, the MCCA mechanism fails to achieve the desired goal in the presence of contending non-MCCA nodes; this is because non-MCCA nodes are not aware of MCCA reservations and have equal access opportunities during reserved periods. We first present a probabilistic analysis that reveals the extent to which the performance of MCCA may be affected by contending non-MCCA nodes. We then propose eMCCA, which allows MCCA-enabled nodes to enjoy collision-free and guaranteed channel access during reserved periods by means of prioritized and preemptive access mechanisms. Finally, we evaluate the performance of eMCCA through extensive simulations under different network scenarios. The simulation results indicate that eMCCA outperforms other mechanisms in terms of success rate, network throughput, end-to-end delay, packet-loss rate, and mesh coordinated channel access opportunity-utilization.

• ETRI Journal
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• v.45 no.5
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• pp.746-757
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• 2023

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree-based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six-degree-of-freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of stateof-the-art 3D path planning algorithms.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.295-306
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• 2007

It is well known that mathematical solutions for multi-agent planning problems are very difficult to obtain due to the complexity of mutual interactions among multi-agents. Most of the past research results are thus based on the probabilistic completeness. However, the practicality and effectiveness of the solution from the probabilistic completeness is significantly reduced by heavy computational burden. In this paper, we propose a practically applicable solution technique for multi-agent planning problems, which assures a reasonable computation time and a real world application for more than 3 multi-agents, for the case of general shaped paths in agent movement. First, to reduce the computation time, an extended collision map is developed and utilized for detecting potential collisions and obtaining collision-free solutions for multi-agents. Second, a priority for multi-agents is considered for successive and interactive modifications of the agent movements with lower priority. Various solutions using speed reduction and time delay of the relevant agents are investigated and compared in terms of the computation time. A practical implementation is finally provided for three different types of agents to emphasize the effectiveness of the proposed interactive approach to multi-agent planning problems.

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

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.3
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• pp.27-33
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• 2010

The goal for the CD&R system is to predict that a conflict is going to occur in the future, communicate the detected conflict to a human operator, and, in some cases, assist in the resolution of the conflict situation. To provide insight into different methods of conflict detection and resolution, a literature review of previous research models and current developmental and operational systems was performed. This paper focuses only on the specific attributes of each model, not on the depth to which a model has been analyzed, validated, or accepted. Thus, care should be taken to remember that a model that seems to be simple according to our categorization scheme may be significantly more viable than an apparently sophisticated model.