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Journal articles on the topic 'Global path planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Zelek, J. S., and M. D. Levine. "Local-global concurrent path planning and execution." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 6 (2000): 865–70. http://dx.doi.org/10.1109/3468.895924.

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2

Sánchez Miralles, Álvaro, and Miguel Ángel Sanz Bobi. "Global Path Planning in Gaussian Probabilistic Maps." Journal of Intelligent and Robotic Systems 40, no. 1 (May 2004): 89–102. http://dx.doi.org/10.1023/b:jint.0000034339.13257.e6.

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3

Bostrom-Rost, Per, Daniel Axehill, and Gustaf Hendeby. "On Global Optimization for Informative Path Planning." IEEE Control Systems Letters 2, no. 4 (October 2018): 833–38. http://dx.doi.org/10.1109/lcsys.2018.2849559.

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4

Panov, Stojanche, and Saso Koceski. "Metaheuristic global path planning algorithm for mobile robots." International Journal of Reasoning-based Intelligent Systems 7, no. 1/2 (2015): 35. http://dx.doi.org/10.1504/ijris.2015.070910.

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5

Xie, Shaorong, Peng Wu, Hengli Liu, Peng Yan, Xiaomao Li, Jun Luo, and Qingmei Li. "A novel method of unmanned surface vehicle autonomous cruise." Industrial Robot: An International Journal 43, no. 1 (January 18, 2016): 121–30. http://dx.doi.org/10.1108/ir-05-2015-0097.

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Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

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Lv, Taizhi, Chunxia Zhao, and Jiancheng Bao. "A Global Path Planning Algorithm Based on Bidirectional SVGA." Journal of Robotics 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8796531.

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For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.

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Li, Xingyu, Bo Tang, John Ball, Matthew Doude, and Daniel W. Carruth. "Rollover-Free Path Planning for Off-Road Autonomous Driving." Electronics 8, no. 6 (May 31, 2019): 614. http://dx.doi.org/10.3390/electronics8060614.

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Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

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Huang, Chen, and Jiyou Fei. "UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 06 (February 21, 2018): 1859008. http://dx.doi.org/10.1142/s0218001418590085.

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Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

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9

Song, Xiaoru, Song Gao, C. B. Chen, Kai Cao, and Jiaoru Huang. "A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot." International Journal of Computers Communications & Control 13, no. 6 (November 29, 2018): 1032–46. http://dx.doi.org/10.15837/ijccc.2018.6.3153.

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Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.

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Niu, Chuanhu, Aijuan Li, Xin Huang, Wei Li, and Chuanyan Xu. "Research on Global Dynamic Path Planning Method Based on Improved A ∗ Algorithm." Mathematical Problems in Engineering 2021 (August 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/4977041.

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Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.

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11

Chesi, G., and Y. S. Hung. "Global Path-Planning for Constrained and Optimal Visual Servoing." IEEE Transactions on Robotics 23, no. 5 (October 2007): 1050–60. http://dx.doi.org/10.1109/tro.2007.903817.

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12

Seereeram, S., and J. T. Wen. "A global approach to path planning for redundant manipulators." IEEE Transactions on Robotics and Automation 11, no. 1 (1995): 152–60. http://dx.doi.org/10.1109/70.345948.

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13

Santa, Fernando Martinez, Edwar Jacinto Gomez, and Holman Montiel Ariza. "Global Path Planning for Mobile Robots using Image Skeletonization." Indian Journal of Science and Technology 10, no. 14 (April 1, 2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i14/112175.

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14

Qu, Yaohong, Yintao Zhang, and Youmin Zhang. "A Global Path Planning Algorithm for Fixed-wing UAVs." Journal of Intelligent & Robotic Systems 91, no. 3-4 (November 4, 2017): 691–707. http://dx.doi.org/10.1007/s10846-017-0729-9.

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15

Han, Du-Hyun, Yeong-Dae Kim, and Ju-Yong Lee. "Multiple-criterion shortest path algorithms for global path planning of unmanned combat vehicles." Computers & Industrial Engineering 71 (May 2014): 57–69. http://dx.doi.org/10.1016/j.cie.2014.02.013.

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16

Lee, Joonwoo. "Heterogeneous-ants-based path planner for global path planning of mobile robot applications." International Journal of Control, Automation and Systems 15, no. 4 (July 20, 2017): 1754–69. http://dx.doi.org/10.1007/s12555-016-0443-6.

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17

Han, Wen Chao, Lun Li, You Bin Lai, and Fu Yu Wang. "An Algorithm of Global Path Planning Applied for Rapid Prototyping." Advanced Materials Research 765-767 (September 2013): 817–20. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.817.

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This paper presents an algorithm to develop a path planning applied for the rapid prototyping. Filling the layer region completely is the key to product accuracy of the part ,which is depending on path planning. It can improve the effective of the scanning speed during prototyping. It divides three steps. First of all, it uses the zigzag technology to plan the scanning path, then links the different path on the same layer based on kd-tree technology and links pathes on the different layer at last. In order to diminish the deformation and shrinkage during the process prototyping, it uses the reversed orientation in the different layer. The correctness of the algorithm is verified by experiment. Compared with the rest algorithm , it is more rapid and accurate.

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18

Bai, Xiong, Haikun Jiang, Junjie Cui, Kuan Lu, Pengyun Chen, and Ming Zhang. "UAV Path Planning Based on Improved A ∗ and DWA Algorithms." International Journal of Aerospace Engineering 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/4511252.

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This work proposes a path planning algorithm based on A ∗ and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A ∗ algorithm is improved based on UAV’s flight environment information and motion constraints. Further, the DWA algorithm’s limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV’s safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path’s efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved A ∗ and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.

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Yu, Zihan, and Linying Xiang. "NPQ-RRT ∗ : An Improved RRT ∗ Approach to Hybrid Path Planning." Complexity 2021 (February 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/6633878.

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In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT ∗ with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.

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20

Wang, Chun Mei, and Feng Shan Huang. "A Method of Coordinate Measuring Path Planning Based on Polychromatic Sets." Applied Mechanics and Materials 599-601 (August 2014): 680–83. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.680.

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In order to improve the intelligent level of path planning in coordinate measurement, the algorithm of path planning based on theory of polychromatic sets and ant colony algorithm is proposed. The model of the relation about measured geometrical features, probe angles and inspection planes is established, and the sub path is got by grouping for the global path on the basis of conjunctive operation of polychromatic sets. The global path is optimized by planning the sub paths with ant colony algorithm. The practical example verified the validity and reliability of the proposed method.

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21

Zhuang, Hongchao, Kailun Dong, Yuming Qi, Ning Wang, and Lei Dong. "Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles." Applied Sciences 11, no. 16 (August 11, 2021): 7378. http://dx.doi.org/10.3390/app11167378.

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In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

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22

Wang, Dong, Jie Zhang, Jiucai Jin, Deqing Liu, and Xingpeng Mao. "Rapid global path planning algorithm for unmanned surface vehicles in large-scale and multi-island marine environments." PeerJ Computer Science 7 (June 29, 2021): e612. http://dx.doi.org/10.7717/peerj-cs.612.

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A global path planning algorithm for unmanned surface vehicles (USVs) with short time requirements in large-scale and complex multi-island marine environments is proposed. The fast marching method-based path planning for USVs is performed on grid maps, resulting in a decrease in computer efficiency for larger maps. This can be mitigated by improving the algorithm process. In the proposed algorithm, path planning is performed twice in maps with different spatial resolution (SR) grids. The first path planning is performed in a low SR grid map to determine effective regions, and the second is executed in a high SR grid map to rapidly acquire the final high precision global path. In each path planning process, a modified inshore-distance-constraint fast marching square (IDC-FM2) method is applied. Based on this method, the path portions around an obstacle can be constrained within a region determined by two inshore-distance parameters. The path planning results show that the proposed algorithm can generate smooth and safe global paths wherein the portions that bypass obstacles can be flexibly modified. Compared with the path planning based on the IDC-FM2 method applied to a single grid map, this algorithm can significantly improve the calculation efficiency while maintaining the precision of the planned path.

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23

Dirik, Mahmut, Oscar Castillo, and Adnan Fatih Kocamaz. "Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control." Axioms 8, no. 2 (May 10, 2019): 58. http://dx.doi.org/10.3390/axioms8020058.

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Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.

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24

Kim, Jo-Hwan, Man-Seok Kim, Min-Koo Choi, and Jong-Wook Kim. "Optimized Global Path Planning of a Mobile Robot Using uDEAS." Journal of Korean Institute of Intelligent Systems 21, no. 2 (April 25, 2011): 268–75. http://dx.doi.org/10.5391/jkiis.2011.21.2.268.

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Yoon, Hee-Sang, and Tae-Hyoung Park. "Path Planning for Autonomous Mobile Robots by Modified Global DWA." Transactions of The Korean Institute of Electrical Engineers 60, no. 2 (February 1, 2011): 389–97. http://dx.doi.org/10.5370/kiee.2011.60.2.389.

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26

LI, Tiancheng, Shudong SUN, and Yang GAO. "Fan-shaped Grid Based Global Path Planning for Mobile Robot." ROBOT 32, no. 4 (August 13, 2010): 547–52. http://dx.doi.org/10.3724/sp.j.1218.2010.00547.

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27

Kenefic, Richard. "Path Planning for Global-Positional-System-Guided Indirect Fire Weapons." Journal of Aerospace Computing, Information, and Communication 5, no. 11 (November 2008): 479–89. http://dx.doi.org/10.2514/1.36888.

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28

Xing, Weiwei, Xiang Wei, and Wei Lu. "Weighted time-based global hierarchical path planning in dynamic environment." Transactions of Tianjin University 20, no. 3 (June 2014): 223–31. http://dx.doi.org/10.1007/s12209-014-2377-5.

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29

Agrawal, O. P., and Y. Xu. "On the global optimum path planning for redundant space manipulators." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 9 (1994): 1306–16. http://dx.doi.org/10.1109/21.310507.

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30

Tang, Ping, and YiMin Yang. "Global Path Planning and Dynamic Eluding Obstacles in Soccer Game." IFAC Proceedings Volumes 34, no. 22 (November 2001): 232–35. http://dx.doi.org/10.1016/s1474-6670(17)32943-9.

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31

Ozcan, Cumhur Yigit, Ebru Akcapinar Sezer, and Murat Haciomeroglu. "A time‐based global path planning strategy for crowd navigation." Computer Animation and Virtual Worlds 30, no. 2 (November 20, 2018): e1864. http://dx.doi.org/10.1002/cav.1864.

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32

Howard, Ayanna, Homayoun Seraji, and Barry Werger. "Global and regional path planners for integrated planning and navigation." Journal of Robotic Systems 22, no. 12 (2005): 767–78. http://dx.doi.org/10.1002/rob.20098.

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33

Zhu, Zexuan, Fangxiao Wang, Shan He, and Yiwen Sun. "Global path planning of mobile robots using a memetic algorithm." International Journal of Systems Science 46, no. 11 (October 9, 2013): 1982–93. http://dx.doi.org/10.1080/00207721.2013.843735.

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Wen, Zhi-qiang, and Zi-xing Cai. "Global path planning approach based on ant colony optimization algorithm." Journal of Central South University of Technology 13, no. 6 (December 2006): 707–12. http://dx.doi.org/10.1007/s11771-006-0018-4.

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35

Skačkauskas, Paulius, and Edgar Sokolovskij. "Analysis of the Hybrid Global Path Planning Algorithm for Different Environments." Transport and Telecommunication Journal 20, no. 1 (February 1, 2019): 1–11. http://dx.doi.org/10.2478/ttj-2019-0001.

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Abstract To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.

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Ma, Xiliang, and Ruiqing Mao. "Path planning for coal mine robot to avoid obstacle in gas distribution area." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141775150. http://dx.doi.org/10.1177/1729881417751505.

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As the explosion-proof safety level of coal mine robot has not yet reached the level of intrinsic safety “ia,” therefore, path planning methods for coal mine robot to avoid the dangerous area of gas are necessary. To avoid a secondary explosion when the coal mine robot passes through gas hazard zones, a path planning method is proposed, considering the gas concentration distributions. The path planning method is composed of two steps in total: the global path planning and the local path adjustment. First, the global working path for coal mine robot is planed based on the Dijkstra algorithm and the ant colony algorithm. Second, with consideration of the dynamic environment, when hazardous gas areas distribute over the planed working path again, local path adjustments are carried out with the help of a proposed local path adjustment method. Lastly, experiments are conducted in a roadway after accident, which verify the effectiveness of the proposed path planning method.

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Yu, Xin-Yi, Zhen-Yong Fan, Lin-Lin Ou, Feng Zhu, and Yong-Kui Guo. "Optimal Path Planning Satisfying Complex Task Requirement in Uncertain Environment." Robotica 37, no. 11 (April 8, 2019): 1956–70. http://dx.doi.org/10.1017/s0263574719000377.

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SummaryRobots often need to accomplish some complex tasks such as surveillance, response and obstacle avoidance. In this paper, a dynamic search method is proposed to generate optimal robot trajectories satisfying complex task requirement in uncertain environment. The LTL-A* algorithm is presented to generate a global optimal path and the A* algorithm is provided to modify the global optimal path. The task is specified by a linear temporal logic (LTL) formula, and a weighted transition system according to the known information in uncertain environment is modeled to describe the robot motion. Subsequently, a product automaton is constructed by combining the transition system with the task requirement. Based on the product automaton, the LTL-A* algorithm is proposed to generate a global optimal path. The local path planning based on the A* algorithm is employed to deal with the environment change during the process of tracking the global optimal path for the robot. The results of the simulation and experiments show that the proposed method can not only meet the complex task requirement in uncertain environment but also improve the search efficiency.

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38

Wang, Huan, and Yu Lian Jiang. "Robotic Fish Path Planning Based on an Improved A* Algorithm." Applied Mechanics and Materials 336-338 (July 2013): 968–72. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.968.

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Applying the global path planning to traditional A* algorithm in a complex environment and a lot of obstacles will result in an infinite loop because there are too many search data. To resolve this problem, this paper provides a new divide-and-rule path planning method which is based on improved A* algorithm. It uses several transition points to divide the entire grid map areas into several sub-regions. We set different speeds in each sub-region for local path planning. Thus the complex global path planning is turned into some simple local path planning. It reduces the search data of A* algorithm and avoids falling into the infinite loop. By this method, this paper designs the path planning of heading the ball, and smoothes the orbit. The simulation results show that the improved A* algorithm is better and more effective than the traditional one.

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39

Zhang, Jing, Jiwu Li, Hongwei Yang, Xin Feng, and Geng Sun. "Complex Environment Path Planning for Unmanned Aerial Vehicles." Sensors 21, no. 15 (August 3, 2021): 5250. http://dx.doi.org/10.3390/s21155250.

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Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

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Ali, Mohammed AH, and Musa Mailah. "Laser simulator." International Journal of Advanced Robotic Systems 15, no. 5 (September 1, 2018): 172988141880472. http://dx.doi.org/10.1177/1729881418804726.

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A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoi diagram with fast marching and PointBug algorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.

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Lemos, Randerson, Olmer Garcia, and Janito Vaqueiro Ferreira. "Local and Global Path Generation for Autonomous Vehicles Using Splines." Ingeniería 21, no. 2 (May 26, 2016): 188–200. http://dx.doi.org/10.14483/udistrital.jour.reving.2016.2.a05.

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Context: Before autonomous vehicles being a reality in daily situations, outstanding issues regarding the techniques of autonomous mobility must be solved. Hence, relevant aspects of a path planning for terrestrial vehicles are shown.Method: The approached path planning technique uses splines to generate the global route. For this goal, waypoints obtained from online map services are used. With the global route parametrized in the arc-length, candidate local paths are computed and the optimal one is selected by cost functions.Results: Different routes are used to show that the number and distribution of waypoints are highly correlated to a satisfactory arc-length parameterization of the global route, which is essential to the proper behavior of the path planning technique.Conclusions: The cubic splines approach to the path planning problem successfully generates the global and local paths. Nevertheless, the use of raw data from the online map services showed to be unfeasible due the consistency of the data. Hence, a preprocessing stage of the raw data is proposed to guarantee the well behavior and robustness of the technique.

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Yuan, Zhiheng, Zhengmao Yang, Lingling Lv, and Yanjun Shi. "A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem." Electronics 9, no. 9 (August 20, 2020): 1351. http://dx.doi.org/10.3390/electronics9091351.

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Avoiding the multi-automated guided vehicle (AGV) path conflicts is of importance for the efficiency of the AGV system, and we propose a bi-level path planning algorithm to optimize the routing of multi-AGVs. In the first level, we propose an improved A* algorithm to plan the AGV global path in the global topology map, which aims to make the path shortest and reduce the AGV path conflicts as much as possible. In the second level, we present the dynamic rapidly-exploring random trees (RRT) algorithm with kinematic constraints to obtain the passable local path with collisions in the local grid map. Compared to the Dijkstra algorithm and classic A* algorithm, the simulation results showed that the proposed bi-level path planning algorithm performed well in terms of the search efficiency, significantly reducing the incidence of multiple AGV path conflicts.

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Roque, Waldir L., and Dionísio Doering. "Trajectory planning for lab robots based on global vision and Voronoi roadmaps." Robotica 23, no. 4 (June 14, 2005): 467–77. http://dx.doi.org/10.1017/s0263574704001183.

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This paper discusses the techniques and their applications in the development of a path planning system composed of three modules, namely: global vision (GVM), trajectory planning (TPM) and navigation control (NCM). The GVM captures and processes the workspace image to identify the obstacle and the robot configurations. These configurations are used by the TPM to generate the Voronoi roadmap, to compute the maximal clearance shortest feasible path and the visibility pathway between two configurations. The NCM controls the robot functionalities and navigation. To validate the path planning system, three sets of experiments have been conducted using the Lab robot Khepera, which have shown very good results.

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Vasile, Cristian Ioan, Xiao Li, and Calin Belta. "Reactive sampling-based path planning with temporal logic specifications." International Journal of Robotics Research 39, no. 8 (June 4, 2020): 1002–28. http://dx.doi.org/10.1177/0278364920918919.

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We develop a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The mission specification has two parts: (1) a global specification given as a linear temporal logic (LTL) formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula; and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The off-line algorithm has four main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which implies low complexity for the overall method. Third, it is probabilistically complete. Fourth, under some mild assumptions, it has the best possible complexity bound. The on-line algorithm leverages ideas from LTL monitoring and potential functions to ensure progress towards the satisfaction of the global specification while servicing locally sensed requests. Examples and experimental trials illustrating the usefulness and the performance of the framework are included.

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Zhang, Bao Feng, Ya Chun Wang, and Xiao Ling Zhang. "Mobile Robot Path Planning Based on Ant Colony Optimization." Applied Mechanics and Materials 687-691 (November 2014): 706–9. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.706.

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Global path planning is quoted in this paper. The stoical and global environment has been given to us, which is abstracted with grid method before we build the workspace model of the robot. With the adoption of the ant colony algorithm, the robot tries to find a path which is optimal or optimal-approximate path from the starting point to the destination. The robot with the built-in infrared sensors navigates autonomously to avoid collision the optimal path which has been built, and moves to the object. Based on the MATLAB platform, the simulation results indicate that the algorithm is rapid, simple, efficient and high-performance. Majority of traditional algorithms of the path planning have disadvantages, for instance, the method of artificial potential field is falling into the problem of local minimum value easily. ACO avoids these drawbacks, therefore the convergence period can be extended, and optimal path can be planned rapidly.

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Huang, Min, Ping Ding, and Jiao Xue Huan. "Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms." Applied Mechanics and Materials 418 (September 2013): 15–19. http://dx.doi.org/10.4028/www.scientific.net/amm.418.15.

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Global optimal path planning is always an important issue in mobile robot navigation. To avoid the limitation of local optimum and accelerate the convergence of the algorithm, a new robot global optimal path planning method is proposed in the paper. It adopts a new transition probability function which combines with the angle factor function and visibility function, and at the same time, sets penalty function by a new pheromone updating model to improve the accuracy of the route searching. The results of computer emulating experiments prove that the method presented is correct and effective, and it is better than the genetic algorithm and traditional ant colony algorithm for global path planning problem.

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Xia, Guoqing, Zhiwei Han, Bo Zhao, Caiyun Liu, and Xinwei Wang. "Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm." Mathematical Problems in Engineering 2019 (April 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/2902170.

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As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

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Zhang, Cheng, Kun Zhang, Ying Qian Zhang, and He Dan. "An Algorithm Applying to NPC Path Planning of Web Games." Applied Mechanics and Materials 556-562 (May 2014): 3420–23. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3420.

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Path planning is the core issues in the artificial intelligence field of games, and how to establish an effective method of path planning is still focused on. A new algorithm based on both the benefits of global path planner methods and local path planner methods and those of A star algorithm and improved artificial potential field method is proposed for NPC path planning in web games. Its feasibleness and effectiveness are also demonstrated by simulation results.

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Muzaffar, Chandra. "The Global Rich and the Global Poor: Seeking the Middle Path." Development 46, no. 4 (December 1, 2003): 29–34. http://dx.doi.org/10.1177/1011637003046004005.

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Yun, Won Soo, Dong Woo Cho, and Yoon Su Baek. "Dynamic Path Planning for Robot Navigation Using Sonor Mapping and Neural Networks." Journal of Dynamic Systems, Measurement, and Control 119, no. 1 (March 1, 1997): 19–26. http://dx.doi.org/10.1115/1.2801208.

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This paper presents a new path planning algorithm for safe navigation of a mobile robot in dynamic as well as static environments. The certainty grid concept is adopted to represent the robot’s surroundings and a simple sensor model is developed for fast acquisition of environmental information. The proposed system integrates global and local path planning and has been implemented in a partially known structured environment without loss of generality for an indoor mobile robot. The global planner finds the initial path based on Dijkstra’s algorithm, while the local planning scheme uses three neural networks to follow the initial global path and avoid colliding with static and moving obstacles. Effectiveness of these algorithms is illustrated through simulation and experiment using a real robot. The results show that the proposed algorithm can be efficiently implemented in a time varying environment.

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