• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.272-281
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• 2013

Traffic Collision Avoidance System (TCAS) is designed to enhance safety in aircraft operations, by reducing the incidences of mid-air collision between aircraft. The current version of TCAS provides only vertical resolution advisory to the pilots, if an aircraft's collision with another is predicted to be imminent, while efforts to include horizontal resolution advisory have been made, as well. This paper introduces a collision resolution algorithm, which includes both vertical and horizontal avoidance maneuvers of aircraft. Also, the paper compares between the performance of the proposed algorithm and that of algorithms with only vertical or horizontal avoidance maneuver of aircraft.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.417-422
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• 1993

Robots working in the multiple robot system can perform the variety of tasks compared to the single robot system, while they are subject to the various tight constraints such as the precise coordination and the mutual collision avoidance during the task execution. In this paper, we provide an algorithm and graphical verification for collision avoidance between two robots working together. The algorithm calculates the minimum time delay for collision avoidance and the graphical verification is performed through the 3-D graphic simulator.

• Journal of Aerospace System Engineering
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• v.12 no.5
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• pp.48-53
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• 2018

In this study, Real-Time Operating System(RTOS) and 3-D collision avoidance algorithm are implemented to three different Miciro Controller Unit(MCU)s and their performances compared. We selected Microchip Technology's ATmega2560, STM's ARM Cortex-M3 and ARM Cortex-M4, because they are widely used. FreeRTOS, an open-source operating system, was also used. The 3D collision avoidance algorithm consists of the vertical and the horizontal avoidance algorithm, which is implemented using C++. The performances of the MCUs were compared with respect to used memory and calculation time. As a result, Cortex-M4's calculation time was the fastest and ATmega2560 used least memory.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.1-8
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• 2008

This paper presents a real-time algorithm for collision detection, collision avoidance and guidance. Three-dimensional point-mass aircraft models are used. For collision detection, conflict probability is calculated by using the Monte-Carlo Simulation. Time at the closest point of approach(CPA) and distance at CPA are needed to determine the collision probability, being compared to certain threshold values. For collision avoidance, one of possible maneuver options is chosen to minimize the collision probability. For guidance to a designated way-point, proportional navigation guidance law is used. Two scenarios on encounter situation are studied to demonstrate the performance of proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.10
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• pp.996-1002
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• 2008

This paper presents the development of a real-time algorithm for collision detection, collision avoidance and guidance to way-point. Three-dimensional point-mass aircraft models are used. For collision detection, time of closest point of approach(CPA) and distance at CPA are compared to threshold values. For collision avoidance, optimal acceleration input which maximizes the terminal relative distance is calculated based on optimal control theory. For guidance to way-point, proportional navigation guidance, the well-known method, is used. Two scenarios of encounter situation are illustrated to validate performance of proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1939-1943
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• 1991

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 shortest 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 of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. 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 Neural 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 proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• 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 Aerospace System Engineering
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• v.15 no.1
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• pp.102-110
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• 2021

Recently, multi-copters equipped with various collision avoidance sensors have been introduced to improve flight stability. LiDAR is used to recognize a three-dimensional position. Multiple cameras and real-time SLAM technology are also used to calculate the relative position to obstacles. A three-dimensional depth sensor with a small process and camera is also used. In this study, a small collision-avoidance multi-copter system capable of in-door flight was developed as a platform for the development of collision avoidance software technology. The multi-copter system was equipped with LiDAR, 3D depth sensor, and small image processing board. Object recognition and collision avoidance functions based on the YOLO algorithm were verified through flight tests. This paper deals with recent trends in drone collision avoidance technology, system design/manufacturing process, and flight test results.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.81-86
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• 2013

This paper presents the lane change system for collision avoidance. The proposed algorithm for the collision avoidance consists of path generation and path following. Using a calculated TTC (Time to Collision), partial braking is operated and collision avoidance path is generated considering relative distance, velocity and acceleration. Based on the collision avoidance path, desired yaw angle and yaw rate are calculated for the automated path following. The lateral controller is designed by a Lyapunov function approach using 3 D.O.F vehicle model and vehicle parameters. The required steering angle is determined from wheel velocity, longitudinal and lateral velocity in order to follow the desired yaw angle and yaw rate. This system is developed MATLAB/Simulink and its performance is evaluated using the commercial software CarSim.

• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.