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Journal articles on the topic 'Avoidance algorithms'

Author: Grafiati
Published: 25 June 2021
Last updated: 1 February 2022

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1

N. Abdulnabi, Ali. "Obstacle Avoidance Techniques for Robot Path Planning." DJES 12, no. 1 (March 1, 2019): 56–65. http://dx.doi.org/10.24237/djes.2019.12107.

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This paper presents a collision-free path planning approaches based on Bézier curve and A-star algorithm for robot manipulator system. The main problem of this work is to finding a feasible collision path planning from initial point to final point to transport the robot arm from the preliminary to the very last within the presence of obstacles, a sequence of joint angles alongside the path have to be determined. To solve this problem several algorithms have been presented among which it can be mention such as Bug algorithms, A-Star algorithms, potential field algorithms, Bézier curve algorithm and intelligent algorithms. In this paper obstacle avoidance algorithms were proposed Bézier and A-Star algorithms, through theoretical studies and simulations with several different cases, it's found verify the effectiveness of the methods suggested. It's founded the Bézier algorithm is smoothing accurate, and effective as compare with the A-star algorithm, but A-star is near to shortest and optimal path to free collision avoidance. The time taken and the elapsed time to traverse from its starting position and to reach the goal are recorded the tabulated results show that the elapsed time with different cases to traverse from the start location to destination using A-star Algorithm is much less as compared to the time taken by the robot using Bézier Algorithm to trace the same path. The robot used was the Lab-Volt of 5DOF Servo Robot System Model 5250 (RoboCIM5250)
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Zhou, Bin, and Jin Fa Qian. "Obstacle Avoidance Control Method of Mobile Robot Motion." Applied Mechanics and Materials 443 (October 2013): 119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.443.119.

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Mobile robot is an intelligent system which can move freely and is scheduled to complete the task in the working environment. Obstacle avoidance of mobile robot is the research hotspot in the control field of the mobile robot. The mobile robot obstacle avoidance methods are classified, including the traditional algorithms and the intelligent algorithms. This paper summarizes the intelligent algorithm in the mobile robot obstacle avoidance technique in the present situation, and the intelligent algorithm which is the most researched in the current. Finally, this paper prospects the development trend of intelligent obstacle avoidance of the robot.
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3

Abhishek, Talabattula Sai, Daniel Schilberg, and Arockia Selvakumar Arockia Doss. "Obstacle Avoidance Algorithms: A Review." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012052. http://dx.doi.org/10.1088/1757-899x/1012/1/012052.

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Abhishek, Talabattula Sai, Daniel Schilberg, and Arockia Selvakumar Arockia Doss. "Obstacle Avoidance Algorithms: A Review." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012052. http://dx.doi.org/10.1088/1757-899x/1012/1/012052.

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5

Gal, Oren. "Unified Trajectory Planning Algorithms for Autonomous Underwater Vehicle Navigation." ISRN Robotics 2013 (June 9, 2013): 1–6. http://dx.doi.org/10.5402/2013/329591.

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This paper presents two efficient methods for obstacle avoidance and path planning for Autonomous Underwater Vehicle (AUV). These methods take into account the dynamic constraints of the vehicle using advanced simulator of AUV considering low level control and stability effects. We present modified visibility graph local avoidance method and a spiral algorithm for obstacle avoidance. The algorithms were tested in challenged scenarios demonstrating safe trajectory planning.
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Kadry, Seifedine, Gennady Alferov, and Viktor Fedorov. "D-Star Algorithm Modification." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 08 (July 17, 2020): 108. http://dx.doi.org/10.3991/ijoe.v16i08.14243.

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One of the most effective methods for solving a navigation problem is the method of constructing a navigation system based on the simultaneous localization and mapping algorithm and obstacle avoidance algorithms. One of the most effective obstacles avoidance algorithms is the D-star algorithm [1, 2, 3], which, despite its effectiveness, has some drawbacks. This modification allows to eliminate some problems arising during the implementation of the navigation system.
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Xu, Zhao, Jinwen Hu, Yunhong Ma, Man Wang, and Chunhui Zhao. "A Study on Path Planning Algorithms of UAV Collision Avoidance." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 1 (February 2019): 100–106. http://dx.doi.org/10.1051/jnwpu/20193710100.

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The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.
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Li, Juan, Jianxin Zhang, Honghan Zhang, and Zheping Yan. "A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments." Sensors 19, no. 13 (June 27, 2019): 2862. http://dx.doi.org/10.3390/s19132862.

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A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.
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Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, Leong Yeng Weng, Hong Kah Foo, Nur Aira Abd Rahman, Nurul Anis Atikah, and Raad Z. Homod. "Development of collision avoidance system for multiple autonomous mobile robots." International Journal of Advanced Robotic Systems 17, no. 4 (July 1, 2020): 172988142092396. http://dx.doi.org/10.1177/1729881420923967.

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This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate.
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Li, Jinxin, Hongbo Wang, Wei Zhao, and Yuanyuan Xue. "Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance." Journal of Advanced Transportation 2019 (April 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/4068783.

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With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.
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11

Lu, Liang, Adrian Carrio, Carlos Sampedro, and Pascual Campoy. "A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor." Remote Sensing 13, no. 9 (May 5, 2021): 1796. http://dx.doi.org/10.3390/rs13091796.

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Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach.
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12

Kandathil, Jom J., Robins Mathew, and Somashekhar S. Hiremath. "Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system." MATEC Web of Conferences 144 (2018): 01012. http://dx.doi.org/10.1051/matecconf/201814401012.

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One of the primary ability of an intelligent mobile robot system is obstacle avoidance. BUG algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithms based on global knowledge, BUG algorithms assume only local knowledge of the environment and a global goal. Among the variations of the BUG algorithms that prevail, BUG-0, BUG-1 and BUG-2 are the more prominent versions. The exhaustive search algorithm present in BUG-1 makes it more reliable and safer for practical applications. Overall, this provides a more predictable and dependable performance. Hence, the essential focus in this paper is on implementing the BUG-1 algorithm across a group of robots to move them from a start location to a target location. The results are compared with the results from BUG-1 algorithm implemented on a single robot. The strategy developed in this work reduces the time involved in moving the robots from starting location to the target location. Further, the paper shows that the total distance covered by each robot in a multi robot-system is always lesser than or equal to that travelled by a single robot executing the same problem.
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13

Bykova, V. S., A. I. Mashoshin, and I. V. Pashkevich. "Safe Navigation Algorithm for Autonomous Underwater Vehicles." Giroskopiya i Navigatsiya 29, no. 1 (2021): 97–110. http://dx.doi.org/10.17285/0869-7035.0058.

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Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.
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14

Zhang, Y. J., F. Du, J. Wang, L. S. Ke, M. Wang, Y. Hu, M. Yu, G. H. Li, and A. Y. Zhan. "A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics." Complexity 2020 (March 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/1616420.

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Aiming at the requirements of vehicle safety collision avoidance system, a safety collision avoidance algorithm based on environmental characteristics and driver characteristics is proposed. By analyzing the relationship between collision avoidance time and the environment, a safety time model is established. In the established safety time model, parameters based on driver characteristics are added, which increases the flexibility of the algorithm. The algorithm can adapt to more different driving conditions and give appropriate warning thresholds. After simulation and comparison with other algorithms, the algorithm proposed in this paper can satisfied the requirements of reducing vehicle collision risk. The effectiveness and feasibility of the algorithm are verified, and the safety of vehicle driving can be improved.
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15

Wu, Qing, Xudong Shen, Yuanzhe Jin, Zeyu Chen, Shuai Li, Ameer Hamza Khan, and Dechao Chen. "Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles." Sensors 19, no. 8 (April 12, 2019): 1758. http://dx.doi.org/10.3390/s19081758.

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Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.
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16

Wu, Jun Hui, Tong Di Qin, Jie Chen, Hui Ping Si, Kai Yan Lin, Qiang Zhou, and Chi Bin Zhang. "Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot." Advanced Materials Research 756-759 (September 2013): 497–503. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.497.

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In order to solve the problems of complete coverage path and obstacle avoidance with the mobile robot, the complete coverage planning was described first, and then the algorithm of the complete coverage path planning was analyzed. The complete traversal algorithm and the obstacle avoidance strategy of the robot around the barrier were put forward. Finally, the traversal control flow chart of the traversal robot implemented in Single Chip Microcomputer (SCM) was obtained. After the above analysis, the algorithm was simple, practical, and low repeatability, and high efficiency. The algorithms could effectively solve the difficulty of complete coverage path and obstacle avoidance with the robot.
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17

Seong, Jae-Dong, and Hae-Dong Kim. "Optimization of collision avoidance maneuver planning for cluster satellites in space debris explosion situation." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 3 (December 20, 2016): 407–22. http://dx.doi.org/10.1177/0954410016682270.

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In this study, the conjunction situation when a number of explosion fragments approach four cluster satellites was simulated, and an optimum avoidance maneuver plan that mitigates this risk was established. The orbits of four deputy satellites around a virtual chief satellite were defined using the Hill–Clohessy–Wiltshire equation, and NASA’s breakup model was applied to similar altitude rocket body to simulate explosions situation. The distribution of 230 explosion fragments after an explosion was simulated, and the size and direction of the Delta-V were applied to each fragment. In this situation, the collision avoidance maneuver planning strategy that uses heuristic algorithm was proposed to establish efficient avoidance maneuver plan for approaching fragments after the explosion. Avoidance maneuver plans using four heuristic algorithms were established to minimize the fuel consumption of the four cluster satellites within several constraints including conjunction risk, ground track and formation between cluster satellites. Eight simulations were performed with different combinations of the four heuristic algorithms and different generations and populations. As a result, all avoidance maneuver plan satisfied conjunction risk constraint as well as the other complex constraints. In particular, particle swarm optimization–gravitational search algorithm established the most efficient maneuver plan with the least fuel consumption and fastest convergence speed.
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18

Han-Pang, Huang, and Lee Pei-Chien. "A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots." Robotica 10, no. 3 (May 1992): 217–27. http://dx.doi.org/10.1017/s0263574700007955.

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SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.
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Ajeil, Fatin Hassan, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092949. http://dx.doi.org/10.1177/1729881420929498.

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The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.
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Abdul Rahman, Rahayu, Suraya Masrom, Normah Omar, and Maheran Zakaria. "An application of machine learning on corporate tax avoidance detection model." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 721. http://dx.doi.org/10.11591/ijai.v9.i4.pp721-725.

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Corporate tax avoidance reduces government revenues which could limit country development plans. Thus, the main objectives of this study is to establish a rigorous and effective model to detect corporate tax avoidance to assist government to prevent such practice. This paper presents the fundamental knowledge on the design and implementation of machine learning model based on five selected algorithms tested on the real dataset of 3,365 Malaysian companies listed on bursa Malaysia from 2005 to 2015. The performance of each machine learning algorithms on the tested dataset has been observed based on two approaches of training. The accuracy score for each algorithm is better with the cross-validation training approach. Additionationally, with the cross-validation training approach, the performances of each machine learning algorithm were tested on different group of features selection namely industry, governance, year and firm characteristics. The findings indicated that the machine learning models present better reliability with industry, governance and firm characteristics features rather than single year determinant mainly with the Random Forest and Logistic Regression algorithms.
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21

Ramadhan, Duaa, Abdulmuttalib Rashid, and Osama Rashid. "Two Dimensional Path Planning with Static Polygon Obstacles Avoidance." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 65–72. http://dx.doi.org/10.37917/10.37917/ijeee.sceeer.3rd.10.

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This paper presents the designing of path planning system in an environment contains a set of static polygon obstacles localized and distributed randomly by using differential drive mobile robot. In this paper the designed algorithm (two dimensional path planning algorithm) is proposed in order of investigate the path planning of mobile robot with free collision using the visibility binary tree algorithm. The suggested algorithm is compared with the virtual circles tangents algorithm in the time of arrival and the longest of the path to the target. The aim of this paper is to get an algorithm has better performance than the other algorithms and get less time of arrival and shortest path with free collision.
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Ramadhan, Duaa, Abdulmuttalib Rashid, and Osama Rashid. "Two Dimensional Path Planning with Static Polygon Obstacles Avoidance." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 65–72. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.10.

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This paper presents the designing of path planning system in an environment contains a set of static polygon obstacles localized and distributed randomly by using differential drive mobile robot. In this paper the designed algorithm (two dimensional path planning algorithm) is proposed in order of investigate the path planning of mobile robot with free collision using the visibility binary tree algorithm. The suggested algorithm is compared with the virtual circles tangents algorithm in the time of arrival and the longest of the path to the target. The aim of this paper is to get an algorithm has better performance than the other algorithms and get less time of arrival and shortest path with free collision.
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23

Ramasubramanian, M., S. Neelakrishnan, S. Sainath, and V. Krishnaven. "Prototype Implementation of Vehicle Collision Avoidance System Algorithms." Applied Mathematics & Information Sciences 11, no. 5 (September 1, 2017): 1407–17. http://dx.doi.org/10.18576/amis/110518.

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24

Liu, Hui, and Yinjing Guo. "Intelligent obstacle avoidance algorithms for autonomous underwater vehicle." Journal of Physics: Conference Series 1693 (December 2020): 012223. http://dx.doi.org/10.1088/1742-6596/1693/1/012223.

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25

Chitra Pradeep, Yazhini, Kendrick Amezquita-Semprun, Manuel Del Rosario, and Peter C. Y. Chen. "ThePcmetric: A performance measure for collision avoidance algorithms." Robotics and Autonomous Systems 109 (November 2018): 125–38. http://dx.doi.org/10.1016/j.robot.2018.08.005.

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26

Hsieh, Fu-Shiung. "Robustness of deadlock avoidance algorithms for sequential processes." Automatica 39, no. 10 (October 2003): 1695–706. http://dx.doi.org/10.1016/s0005-1098(03)00176-6.

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Hafner, Michael R., Drew Cunningham, Lorenzo Caminiti, and Domitilla Del Vecchio. "Cooperative Collision Avoidance at Intersections: Algorithms and Experiments." IEEE Transactions on Intelligent Transportation Systems 14, no. 3 (September 2013): 1162–75. http://dx.doi.org/10.1109/tits.2013.2252901.

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Hsieh, Fu-Shiung. "ROBUSTNESS OF DEADLOCK AVOIDANCE ALGORITHMS FOR SEQUENTIAL PROCESSES." IFAC Proceedings Volumes 35, no. 1 (2002): 223–28. http://dx.doi.org/10.3182/20020721-6-es-1901.00039.

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Rajamani, Kushwin, Adam S. Goldberg, and Bruce L. Wilkoff. "Shock Avoidance and the Newer Tachycardia Therapy Algorithms." Cardiology Clinics 32, no. 2 (May 2014): 191–200. http://dx.doi.org/10.1016/j.ccl.2014.01.002.

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Wu, Peng, Shaorong Xie, Hengli Liu, Ming Li, Hengyu Li, Yan Peng, Xiaomao Li, and Jun Luo. "Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring." Industrial Robot: An International Journal 44, no. 1 (January 16, 2017): 64–74. http://dx.doi.org/10.1108/ir-04-2016-0127.

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Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.
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Kuczkowski, Łukasz, and Roman Śmierzchalski. "Comparison of Single and Multi-Population Evolutionary Algorithm for Path Planning in Navigation Situation." Solid State Phenomena 210 (October 2013): 166–77. http://dx.doi.org/10.4028/www.scientific.net/ssp.210.166.

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In this paper a comparison of single and multi-population evolutionary algorithm is presented. Tested algorithms are used to determine close to optimal ship paths in collision avoidance situation. For this purpose a path planning problem is defined. A specific structure of the individual path and fitness function is presented. Principle of operation of single-population and multi-population evolutionary algorithm is described. Using presented algorithms the simulations on three close to real sea environments were performed. Regardless of the test situation constant time simulation was maintained. Obtained results are presented in graphical form (sequences of successive stages of the simulation) and in form of table in which the values of fitness function for best individual in each simulation were compared. Undertaken research allow to select evolutionary algorithm that, assuming constant simulation time, will determine a better path in close to real collision avoidance situation at sea.
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Statheros, Thomas, Gareth Howells, and Klaus McDonald Maier. "Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques." Journal of Navigation 61, no. 1 (December 10, 2007): 129–42. http://dx.doi.org/10.1017/s037346330700447x.

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This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).
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Khan, Dr Rafiqul Zaman, and Haider Allamy. "Training Algorithms for Supervised Machine Learning: Comparative Study." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 3 (July 25, 2013): 354–60. http://dx.doi.org/10.24297/ijmit.v4i3.773.

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Supervised machine learning is an important task for learning artificial neural networks; therefore a demand for selected supervised learning algorithms such as back propagation algorithm, decision tree learning algorithm and perceptron algorithm has been arise in order to perform the learning stage of the artificial neural networks. In this paper; a comparative study has been presented for the aforementioned algorithms to evaluate their performance within a range of specific parameters such as speed of learning, overfitting avoidance, and their accuracy. Besides these parameters we have included their benefits and limitations to unveil their hidden features and provide more details regarding their performance. We have found the decision tree algorithm is the best as compared with other algorithms that can solve the complex problems with a remarkable speed.
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Sánchez, Paloma, Rafael Casado, and Aurelio Bermúdez. "Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes." Electronics 9, no. 10 (October 3, 2020): 1632. http://dx.doi.org/10.3390/electronics9101632.

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Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.
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Falanga, Davide, Kevin Kleber, and Davide Scaramuzza. "Dynamic obstacle avoidance for quadrotors with event cameras." Science Robotics 5, no. 40 (March 18, 2020): eaaz9712. http://dx.doi.org/10.1126/scirobotics.aaz9712.

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Today’s autonomous drones have reaction times of tens of milliseconds, which is not enough for navigating fast in complex dynamic environments. To safely avoid fast moving objects, drones need low-latency sensors and algorithms. We departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds. Our approach exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles. Standard vision algorithms cannot be applied to event cameras because the output of these sensors is not images but a stream of asynchronous events that encode per-pixel intensity changes. Our resulting algorithm has an overall latency of only 3.5 milliseconds, which is sufficient for reliable detection and avoidance of fast-moving obstacles. We demonstrate the effectiveness of our approach on an autonomous quadrotor using only onboard sensing and computation. Our drone was capable of avoiding multiple obstacles of different sizes and shapes, at relative speeds up to 10 meters/second, both indoors and outdoors.
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36

Radmanesh, Mohammadreza, Manish Kumar, Paul H. Guentert, and Mohammad Sarim. "Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study." Unmanned Systems 06, no. 02 (April 2018): 95–118. http://dx.doi.org/10.1142/s2301385018400022.

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Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.
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Shu, Xiao Meng, Da Ming Jiang, and Lian Dai. "Study on Improved APF Algorithm for Autonomous Mobile Robot." Applied Mechanics and Materials 519-520 (February 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1337.

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In algorithms of obstacle avoidance for autonomous mobile robot, APF algorithm is simple, real-time and smooth, but has some limitations for solving problems. For example, the local minimum point may trap mobile robots before reaching its goal. Even though many improved APF algorithms have been put forward, few articles describe the process in detail to show how these algorithms are applied. Considering above factors, this paper focuses on embodiment of abstract improved theory for APF algorithm by showing some changes with formulas and parameters. The whole work has been done in simulation environment. According to the results this paper draws a conclusion.
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Arokiasami, Willson Amalraj, Prahlad Vadakkepat, Kay Chen Tan, and Dipti Srinivasan. "Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework." Unmanned Systems 06, no. 04 (October 2018): 231–50. http://dx.doi.org/10.1142/s2301385018500061.

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Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.
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Zhang, Lin, Yingjie Zhang, Manni Zeng, and Yangfan Li. "Robot navigation based on improved A* algorithm in dynamic environment." Assembly Automation 41, no. 4 (July 8, 2021): 419–30. http://dx.doi.org/10.1108/aa-07-2020-0095.

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Purpose The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time. Design/methodology/approach To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms. Findings The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment. Originality/value This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.
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DUATO, JOSÉ. "CHANNEL CLASSES: A NEW CONCEPT FOR DEADLOCK AVOIDANCE IN WORMHOLE NETWORKS." Parallel Processing Letters 02, no. 04 (December 1992): 347–54. http://dx.doi.org/10.1142/s0129626492000490.

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In previous papers, we have developed the theoretical background for the design of deadlock-free adaptive routing algorithms for store-and-forward and wormhole networks. Some definitions and theorems have been proposed, developing conditions to verify that an adaptive algorithm is deadlock-free, even when there are cyclic dependencies between channels. Also, two design methodologies have been proposed. In this paper, we propose a partial order between channels as well as an equivalence relation. This relation splits the set of channels into equivalence classes. Then, we extend our previous theory by considering equivalence classes (channel classes) instead of channels. This extension drastically simplifies the verification of deadlock freedom for adaptive routing algorithms with cyclic dependencies between channels. Finally, we present an example.
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Wen, Shuhuan, Xueheng Hu, Zhen Li, Hak Keung Lam, Fuchun Sun, and Bin Fang. "NAO robot obstacle avoidance based on fuzzy Q-learning." Industrial Robot: the international journal of robotics research and application 47, no. 6 (October 16, 2019): 801–11. http://dx.doi.org/10.1108/ir-01-2019-0002.

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Purpose This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment. Design/methodology/approach The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm. Findings Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective. Originality/value The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.
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42

Faris, Hossam, Ibrahim Aljarah, Nailah Al-Madi, and Seyedali Mirjalili. "Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm." International Journal on Artificial Intelligence Tools 25, no. 06 (October 27, 2016): 1650033. http://dx.doi.org/10.1142/s0218213016500330.

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Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.
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43

Stojan, Ivo. "A Dynamic Interference-Avoidance Algorithm for Frequency Hopping Systems." Journal of Communications Software and Systems 5, no. 2 (June 22, 2009): 74. http://dx.doi.org/10.24138/jcomss.v5i2.208.

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In this paper we investigate an algorithm for the Adaptive Frequency Hopping mechanism that is used by frequency dynamic systems to mitigate interference from othersystems. With this algorithm we introduce several improvements in relation to the existing algorithms that are based on the approach of using Packet Error Rate as the means for channel classification. One is the use of a single criterion for channel classification regardless of the dynamics of interfering systems, which adds more flexibility and reduces the risk of erroneous channel classification. The second is the introduction of the concept of channel probing which ensures that channels that areexcluded from the hopset are not used until they are clear from interference. The third improvement is the parameterization of the algorithm, which enables the control of the trade off between the main achievements of the algorithm: throughput and quickness of adaptation to changing interference. We show these achievements of the proposed algorithm through simulation.
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Lazarowska, Agnieszka. "Decision support system for collision avoidance at sea." Polish Maritime Research 19, Special (October 1, 2012): 19–24. http://dx.doi.org/10.2478/v10012-012-0018-2.

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ABSTRACT The paper presents design and realization of computer decision support system in collision situations of passage with greater quantity of met objects. The system was implemented into the real ship electro-navigational system onboard research and training ship m/v HORYZONT II. The radar system with Automatic Radar Plotting Aid constitutes a source of input data for algorithm determining safe trajectory of a ship. The article introduces radar data transmission details. The dynamic programming algorithm is used for the determination of safe optimal trajectory of own ship. The system enables navigational data transmission from radar system and automatic determining of safe manoeuvre or safe trajectory of a ship. Further development of navigator’s decision support system is also presented. Path Planning Subsystem is proposed for the determination of global optimal route between harbours with the use of Ant Colony Optimization algorithms.
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45

Gable, Shelly L., and Joshua Poore. "Which Thoughts Count? Algorithms for Evaluating Satisfaction in Relationships." Psychological Science 19, no. 10 (October 2008): 1030–36. http://dx.doi.org/10.1111/j.1467-9280.2008.02195.x.

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Individuals differ in both their motivation to obtain incentives in their relationships (approach goals) and their motivation to dampen the threats in their relationships (avoidance goals). When evaluating relationship satisfaction, individuals with strong approach goals should weigh positive features in their relationships more heavily than do individuals low in approach goals, and individuals with strong avoidance social goals should weigh negative features more than do individuals with weaker avoidance social goals. In a study testing this idea, participants were randomly signaled several times a day to report their positive (passion) and negative (insecurity) thoughts about their current romantic partner. At the end of the day, they reported their overall relationship satisfaction. The results confirmed the hypotheses: Algorithms used to assess relationship satisfaction differ as a function of goal strength. We discuss the results not in terms of biases in subjective evaluation, but rather in terms of variations in the very definition of satisfaction.
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46

Tsou, Ming-Cheng, Sheng-Long Kao, and Chien-Min Su. "Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts." Journal of Navigation 63, no. 1 (December 1, 2009): 167–82. http://dx.doi.org/10.1017/s037346330999021x.

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When an officer of the watch (OOW) faces complicated marine traffic, a suitable decision support tool could be employed in support of collision avoidance decisions, to reduce the burden and greatly improve the safety of marine traffic. Decisions on routes to avoid collisions could also consider economy as well as safety. Through simulating the biological evolution model, this research adopts the genetic algorithm used in artificial intelligence to find a theoretically safety-critical recommendation for the shortest route of collision avoidance from an economic viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of a ship. Based on this recommendation, an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle will also be provided. A Geographic Information System (GIS) will be used as the platform for display and operation. In order to achieve advance notice of alerts and due preparation for collision avoidance, a Vessel Traffic Services (VTS) operator and the OOW can use this system as a reference to assess collision avoidance at present location.
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47

Migliaccio, G., G. Mengali, and R. Galatolo. "Conflict detection and resolution algorithms for UAVs collision avoidance." Aeronautical Journal 118, no. 1205 (July 2014): 828–42. http://dx.doi.org/10.1017/s0001924000009568.

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Abstract Collision-avoidance is a safety-critical requirement to operate UAVs in non-segregated airspaces. In case of communication problems between a UAV and the corresponding pilot-in-command, a technology is required onboard the UAV to implement a capability to detect and avoid collision-hazards even autonomously. After an introduction to the problem of developing a so-called sense-and-avoid system and its avoid-function, this work presents a solution in terms of algorithms to implement the above capability. To detect and resolve potential mid-air conflicts, a geometric deterministic approach has been utilised: an intruder is modeled trough a moving-ellipsoid and a four-dimensional approach in the time-space domain provides the solution. The approach makes use of kinematics information to detect potential conflicts and to provide actions for conflict resolution, such as speed-changes in intensity and/or direction. The proposed solution also enables the UAV to meet the applicable vertical and horizontal minima of separation and to comply with real-time constraints.
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Ahn, D. S., G. H. Park, G. J. Choi, and S. Y. Jeon. "Researches on Collision Avoidance Algorithms for Autonomous Driving System." Journal of the Korea Society For Power System Engineering 16, no. 1 (February 29, 2012): 84–90. http://dx.doi.org/10.9726/kspse.2012.16.1.084.

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49

Blazewicz, J., D. P. Bovet, J. Brzezinski, G. Gambosi, and M. Talamo. "Optimal centralized algorithms for store-and-forward deadlock avoidance." IEEE Transactions on Computers 43, no. 11 (1994): 1333–38. http://dx.doi.org/10.1109/12.324567.

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50

Brown, Timothy L., John D. Lee, and Daniel V. McGehee. "Human Performance Models and Rear-End Collision Avoidance Algorithms." Human Factors: The Journal of the Human Factors and Ergonomics Society 43, no. 3 (September 2001): 462–82. http://dx.doi.org/10.1518/001872001775898250.

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