Journal articles on the topic 'View Planning Algorithm'
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1
Cui, Zhiqiang, Zhaoyang Liao, Xubin Lin, Kezheng Sun, Taobo Cheng, and Xuefeng Zhou. "Multi-View consistency-based point cloud registration method with low overlap rate." Journal of Physics: Conference Series 2724, no. 1 (March 1, 2024): 012034. http://dx.doi.org/10.1088/1742-6596/2724/1/012034.
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Abstract Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.
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Yuan, Ye, Lei Liu, Yu Kuan Sun, and Jian Ming Wang. "Indoor Mobile-Robot Path Panning Based Bird’s Eye View Images." Applied Mechanics and Materials 654 (October 2014): 167–72. http://dx.doi.org/10.4028/www.scientific.net/amm.654.167.
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Navigation is essential function for a mobile robot to make it navigate in its environment. The navigation based on bird’s eye view images is a newly proposed indoor visual navigation framework, which can help to simplify the navigation problems, such as self-localization, map-building, path planning and other competences. In the paper, a path planning algorithm applied to the indoor visual navigation is proposed. Firstly, three planning conditions are adopted as the constraints for algorithm design, and then the algorithm is explained. Finally, simulation and practical experiments were implemented and the results were analyzed.
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Wang, Haiyan, and Zhiyu Zhou. "A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning." Information 10, no. 3 (March 6, 2019): 99. http://dx.doi.org/10.3390/info10030099.
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Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.
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Davies, Toby, Adrian Pearce, Peter Stuckey, and Harald Søndergaard. "Fragment-Based Planning Using Column Generation." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 10, 2014): 83–91. http://dx.doi.org/10.1609/icaps.v24i1.13628.
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We introduce a novel algorithm for temporal planning in Golog using shared resources, and describe the Bulk Freight Rail Scheduling Problem, a motivating example of such a temporal domain. We use the framework of column generation to tackle complex resource constrained temporal planning problems that are beyond the scope of current planning technology by combining: the global view of a linear programming relaxation of the problem; the strength of search infinding action sequences; and the domain knowledge that can be encoded in a Golog program. We show that our approach significantly outperforms state-of-the-art temporal planning and constraint programming approaches in this domain, in addition to existing temporal Golog implementations. We also apply our algorithm to a temporal variant of blocks-world where our decomposition speeds proof of optimality significantly compared to other anytime algorithms. We discuss the potential of the underlying algorithm being applicable to STRIPS planning, with further work.
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Wang, Ting-Ting, Xue Han, Jun Zhou, and Hua Chen. "Path planning for visual servoing with search algorithm." Advances in Mechanical Engineering 10, no. 1 (January 2018): 168781401775026. http://dx.doi.org/10.1177/1687814017750264.
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To solve the visual servoing tasks in complex environment, a path planning method based on improved rapidly exploring random trees algorithm is proposed. First, the improved rapidly exploring random trees planning method is adopted, which keeps the observed feature points in the field of view. The start node and the desired node are initialized as roots of multi-trees which grow harmoniously to plan path of the robot. Then, the planned path is used to project the three-dimensional target feature points into the image space and obtain the feature trajectory for the image-based visual servoing controller. Finally, the feature trajectory is tracked by the image-based visual servoing controller. The proposed visual servoing design method takes field of view constraints, camera retreat problem, and obstacle avoidance into consideration, which can significantly improve the ability of the robotic manipulator, especially in the narrow space. Simulation and experiment on 6-degree-of-freedom robot are conducted. The results present the effectiveness of the proposed algorithm.
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Qian, Lanmei, Haifei Zhang, Jianlin Qiu, Xudong Zhang, Hassan Fouad, and Torki Altameem. "Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm." Journal of Nanoelectronics and Optoelectronics 18, no. 3 (March 1, 2023): 329–37. http://dx.doi.org/10.1166/jno.2023.3400.
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Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.
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Lou, Na. "Analysis of the Intelligent Tourism Route Planning Scheme Based on the Cluster Analysis Algorithm." Computational Intelligence and Neuroscience 2022 (June 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/3310676.
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In view of the problems of the traditional cluster analysis algorithm such as strong dependence on the initial cluster center, the traditional k-means cluster analysis algorithm is improved and the experiment proves that the improved algorithm has a better clustering effect; in view of the problems of the traditional tourism route planning, the improved k-means cluster analysis algorithm is applied to the intelligent tourism route planning scheme design and an intelligent tourism planning scheme based on the cluster analysis algorithm is proposed; the tourists’ preference metric is fully considered, and the experimental results show that the scheme has certain reasonableness and reference value.
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Yang, Wen Jun, Huai Bin Wang, and Jing Hui Wang. "Research on Path Planning for Mobile Robot Based on Grid and Hybrid of GA/SA." Advanced Materials Research 479-481 (February 2012): 1499–503. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1499.
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Path planning is the kernel problem of the robot technology area. In this paper, the grid method is used to make environmental modeling, Since the Genetic Algorithm (GA) has its immanent limitations and the Simulated Annealing (SA) Algorithm has the advantages in some aspects, combined these two algorithms together just achieve the perfection. In view of this, a hybrid of GA and SA (GA-SA Hybrid) is proposed in this paper to solve path planning problem for mobile robot. The algorithm making the crossover and mutation probability adjusted adaptively and nonlinearly with the completion time, can avoid such disadvantages as premature convergence. The new algorithm has better capability of searching globally and locally. The simulation results demonstrate that the proposed algorithm is valid and effective.
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Banyai, Tamas. "SENSITIVITY ANALYSIS OF WAGNER-WHITIN ALGORITHM." Journal of Production Engineering 25, no. 1 (June 30, 2022): 37–42. http://dx.doi.org/10.24867/jpe-2022-01-037.
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Material requirement planning (MRP) plays an important role in the efficiency improvement of manufacturing companies. The MRP solutions of enterprise resource planning (ERP) systems are influenced by both technological and logistics parameters, but using additional algorithms, like Wagner-Whitin or Silver-Meal heuristics, it is possible to take more parameters into consideration. These heuristics can optimise the results of MRP, especially from ordering, warehousing and transportation costs point of view. Within the frame of this article the impact of process parameters on the result of Wagner-Whitin algorithm are described.
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Hanks, S., and D. S. Weld. "A Domain-Independent Algorithm for Plan Adaptation." Journal of Artificial Intelligence Research 2 (January 1, 1995): 319–60. http://dx.doi.org/10.1613/jair.79.
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The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.
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Kong, Yanzi, Feng Zhu, Haibo Sun, Zhiyuan Lin, and Qun Wang. "A Generic View Planning System Based on Formal Expression of Perception Tasks." Entropy 24, no. 5 (April 20, 2022): 578. http://dx.doi.org/10.3390/e24050578.
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View planning (VP) is a technique that guides the adjustment of the sensor’s postures in multi-view perception tasks. It converts the perception process into active perception, which improves the intelligence and reduces the resource consumption of the robot. We propose a generic VP system for multiple kinds of visual perception. The VP system is built on the basis of the formal description of the visual task, and the next best view is calculated by the system. When dealing with a given visual task, we can simply update its description as the input of the VP system, and obtain the defined best view in real time. Formal description of the perception task includes the task’s status, the objects’ prior information library, the visual representation status and the optimization goal. The task’s status and the visual representation status are updated when data are received at a new view. If the task’s status has not reached its goal, candidate views are sorted based on the updated visual representation status, and the next best view that can minimize the entropy of the model space is chosen as the output of the VP system. Experiments of view planning for 3D recognition and reconstruction tasks are conducted, and the result shows that our algorithm has good performance on different tasks.
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Zheng, Deyan, Chunhui Liu, and Lizhen Huang. "Spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search." Journal of Physics: Conference Series 2187, no. 1 (February 1, 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2187/1/012047.
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Abstract A spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search is proposed in this paper, named STC-MARL, which is based on multi-agent reinforcement learning and allows several UAVs equipped with two types of sensors to learn to complete full view of a field of interest (FOI). The proposed STC-MARL algorithm can make participating UAVs to learn from the environment to complete the full coverage of a specific FOI while minimizing field of views (FOVs) interacted with each other. The experimental results show in detail with simulation that the UAVs in the mission can effectively accomplish the task of spatio-temporal coverage planning.
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Abdulsaheb, Jaafar Ahmed, and Dheyaa Jasim Kadhim. "Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey." Robotics 12, no. 4 (June 27, 2023): 93. http://dx.doi.org/10.3390/robotics12040093.
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The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.
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Kong, Yanzi, Feng Zhu, Yingming Hao, Haibo Sun, Yilin Xie, and Zhiyuan Lin. "An active reconstruction algorithm based on partial prior information." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988142090420. http://dx.doi.org/10.1177/1729881420904203.
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Active reconstruction is an intelligent perception method that achieves object modeling with few views and short motion paths by systematically adjusting the parameters of the camera while ensuring model integrity. Part of the object information is always known for vision tasks in real scenes, and it provides some guidance for the view planning. A two-step active reconstruction algorithm based on partial prior information is presented, which includes rough shape estimation phase and complete object reconstruction phase, and both of them introduce the concept of active vision. An information expression method is proposed that can be used to manually initialize the repository according to specific visual tasks, and then the prior information and detected information are used to plan the next best view online until the object reconstruction is completed. The method is evaluated with simulated experiments and the result is compared with other methods.
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Liu, Ying. "Modeling and Research on Route Planning." Journal of Physics: Conference Series 2294, no. 1 (June 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2294/1/012027.
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Abstract In view of the problem of fixed and mobile threat areas in the navigation of reconnaissance aircraft, this paper proposes to connect the position information with the time information, and build a distance matrix based on the ant colony algorithm to model and solve the optimal navigation route.This article is based on the problem of the threat area in the area where the reconnaissance aircraft is sailing, since the distance between two target points is no longer a straight line distance but a curve distance, so consider building a distance matrix.The simulation experiment results show that the algorithm proposed in this paper can select the optimal path faster and has good accuracy when traversing each target point.
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Zhu, Y., R. Phillips, J. G. Griffiths, W. Viant, A. Mohsen, and M. Bielby. "Recovery of distal hole axis in intramedullary nail trajectory planning." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 216, no. 5 (May 1, 2002): 323–32. http://dx.doi.org/10.1243/09544110260216595.
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In intramedullary nail (IMN) surgical operations, one of the main efforts for surgeons is to find the axes of two distal holes. Two distal holes on an IMN, which are inside the intramedullary canal of a patient's femur, can only be seen in a lateral X-ray view. For the standard surgical procedure, the localization of the distal hole axes is a trial-and-error process which results in a long surgical time and large dose of X-ray exposure. In this paper, an algorithm to derive the three-dimensional position and orientation of the distal hole axis was developed. The algorithm first derives the nail axis through two X-ray images. Then the distal hole axis is calculated through projecting back the hole boundary on the X-ray image from a lateral view to three-dimensional space. A least-squares method is used to determine the centres of the front hole and the back hole through iteration. The algorithm has been tested with real data and it was robust.
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Li, Liangzhi, and Nanfeng Xiao. "Volumetric view planning for 3D reconstruction with multiple manipulators." Industrial Robot: An International Journal 42, no. 6 (October 19, 2015): 533–43. http://dx.doi.org/10.1108/ir-05-2015-0110.
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Purpose – This paper aims to propose a new view planning method which can be used to calculate the next-best-view (NBV) for multiple manipulators simultaneously and build an automated three-dimensional (3D) object reconstruction system, which is based on the proposed method and can adapt to various industrial applications. Design/methodology/approach – The entire 3D space is encoded with octree, which marks the voxels with different tags. A set of candidate viewpoints is generated, filtered and evaluated. The viewpoint with the highest score is selected as the NBV. Findings – The proposed method is able to make the multiple manipulators, equipped with “eye-in-hand” RGB-D sensors, work together to accelerate the object reconstruction process. Originality/value – Compared to the existed approaches, the proposed method in this paper is fast, computationally efficient, has low memory cost and can be used in actual industrial productions where the multiple different manipulators exist. And, more notably, a new algorithm is designed to speed up the generation and filtration of the candidate viewpoints, which can guarantee both speed and quality.
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Okeson, Trent J., Benjamin J. Barrett, Samuel Arce, Cory A. Vernon, Kevin W. Franke, and John D. Hedengren. "Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection." Sensors 19, no. 12 (June 16, 2019): 2703. http://dx.doi.org/10.3390/s19122703.
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This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire dam at high-priority level. This results in approximately 75% reduced flight time and model processing load, while still maintaining high model accuracy where needed. Models display stepped improvement in visual clarity and SfM reconstruction integrity by priority level, with the higher priority regions more accurately modeling smaller and finer features. A resolution map of the final tiered model is included. While this study focuses on multi-scale view planning for optical sensors, the methods potentially extend to other remote sensors, such as aerial LiDAR.
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Zhang, Zhen, Qun Fang, Jinfeng Song, Xiuwei Zhang, and Zhanxia Zhu. "Research on dynamic path planning algorithm of spacecraft cluster based on cooperative particle swarm algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 6 (December 2021): 1222–32. http://dx.doi.org/10.1051/jnwpu/20213961222.
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In order to solve the problem of path planning for the spacecraft cluster to reach the dynamic target point under the premise of considering obstacle avoidance. In view of the fixed search radius, it will be difficult for the spacecraft to find a better value when it is close to the target point. This paper converts the orbital dynamics of each member spacecraft into an optimization problem considering constraints, and proposes an improved CPSO algorithm based on coordination. The path planning method of the traditional particle swarm optimization (CPSO): The dynamic radius search method that changes the search radius by changing the distance between them, and improves the CPSO algorithm based on this. The improved CPSO algorithm autonomously finds the optimal path of each member spacecraft at the current moment through the dynamic search radius, thereby obtaining the optimal solution for the dynamic path planning of the spacecraft cluster in three-dimensional space. The simulation results show that the use of the improved CPSO algorithm can not only obtain the optimal solution to the spacecraft cluster dynamic path planning problem, but also greatly reduce the fuel consumption in its path planning and improve the path stability of each member spacecraft.
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Ma, Xiaolu, Rui Gong, Yibo Tan, Hong Mei, and Chengcheng Li. "Path Planning of Mobile Robot Based on Improved PRM Based on Cubic Spline." Wireless Communications and Mobile Computing 2022 (November 22, 2022): 1–12. http://dx.doi.org/10.1155/2022/1632698.
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In view of the shortcomings of low search efficiency and many path turning points of Probabilistic Roadmaps (PRM), a bidirectional search PRM global path planning algorithm is proposed. The algorithm improves the search connection rules by using the positive and negative directions to search the path alternately, so that the connection of unnecessary nodes reduces, thereby speeding up the efficiency of path planning. Besides, the algorithm incorporates cubic spline interpolation. That will increase the smoothness of path planning and ensure that the mobile robot can realize the path planning task more smoothly and safely. The simulation results show that the improved algorithm can effectively improve the convergence speed and path smoothness of the algorithm. Finally, the improved algorithm is applied to the actual mobile robot navigation experiment. The experimental results have proven that the path planning strategy was able to a superior advantage over traditional PRM in path quality and computational time.
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Jiang, K., L. D. Seneviratne, and S. W. E. Earles. "Three-Dimensional Shortest Path Planning in the Presence of Polyhedral Obstacles." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 210, no. 4 (July 1996): 373–81. http://dx.doi.org/10.1243/pime_proc_1996_210_209_02.
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A new algorithm is presented for solving the three-dimensional shortest path planning (3DSP) problem for a point object moving among convex polyhedral obstacles. It is the first non-approximate three-dimensional path planing algorithm that can deal with more than two polyhedral obstacles. The algorithm extends the visibility graph concept from two dimensions to three dimensions. The two main problems with 3DSP are identifying the edge sequence the shortest path passes through and the turning points of the shortest path. A technique based on projective relationships is presented for identifying the set of visible boundary edges (VBE) corresponding to a given view point over which the shortest path, from the view point to the goal, will pass. VBE are used to construct an initial reduced visibility graph (RVG). Optimization is used to revise the position of the turning points and hence the three-dimensional RVG (3DRVG) and the global shortest path is then selected from the 3DRVG. The algorithm is of computational complexity O(n3vk), where n is the number of verticles, v is the maximum number of vertices on any one obstacle and k is the number of obstacles. The algorithm is applicable only with polyhedral obstacles, as the theorems developed for searching for the turning points of the three-dimensional shortest path are based on straight edges of the obstacles. It needs to be further developed for dealing with arbitrary-shaped obstacles and this would increase the computational complexity. The algorithm is tested using computer simulations and some results are presented.
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Szczepanski, Rafal, Artur Bereit, and Tomasz Tarczewski. "Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality." Energies 14, no. 20 (October 14, 2021): 6642. http://dx.doi.org/10.3390/en14206642.
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Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.
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Zhang, Huangchuang, Qingjun Zhuang, and Ge Li. "Robot Path Planning Method Based on Indoor Spacetime Grid Model." Remote Sensing 14, no. 10 (May 13, 2022): 2357. http://dx.doi.org/10.3390/rs14102357.
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In the context of digital twins, smart city construction and artificial intelligence technology are developing rapidly, and more and more mobile robots are performing tasks in complex and time-varying indoor environments, making, at present, the unification of modeling, dynamic expression, visualization of operation, and wide application between robots and indoor environments a pressing problem to be solved. This paper presents an in-depth study on this issue and summarizes three major types of methods: geometric modeling, topological modeling, and raster modeling, and points out the advantages and disadvantages of these three types of methods. Therefore, in view of the current pain points of robots and complex time-varying indoor environments, this paper proposes an indoor spacetime grid model based on the three-dimensional division framework of the Earth space and innovatively integrates time division on the basis of space division. On the basis of the model, a dynamic path planning algorithm for the robot in the complex time-varying indoor environment is designed, that is, the Spacetime-A* algorithm (STA* for short). Finally, the indoor spacetime grid modeling experiment is carried out with real data, which verifies the feasibility and correctness of the spacetime relationship calculation algorithm encoded by the indoor spacetime grid model. Then, experiments are carried out on the multi-group path planning algorithms of the robot under the spacetime grid, and the feasibility of the STA* algorithm under the indoor spacetime grid and the superiority of the spacetime grid are verified.
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Sheng, Si Qing, and Shao Bo Yang. "Distribution Network Planning Based on Multi-Island Group Strategy Genetic Algorithm." Advanced Materials Research 960-961 (June 2014): 964–68. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.964.
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In view of faults which the traditional genetic algorithm (GA) have such as slow convergence speed and easy to fall into the local optimum. This paper put forward a genetic algorithm which is based on the multi-island group strategy, and applied it to the distribution network planning. The paper has established a planning model which takes the yearly comprehensive cost as objective function and discusses the repair methods of islands, solitary chain and closed-loop to meet with the requirements of grid radial. Finally, the proposed method is planning on a 54-node grid to prove the effectiveness of the algorithm and model.
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Zhu, Zixuan, Chenglong Teng, Yingfeng Cai, Long Chen, Yubo Lian, and Hai Wang. "Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field." World Electric Vehicle Journal 13, no. 11 (October 31, 2022): 203. http://dx.doi.org/10.3390/wevj13110203.
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The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird’s-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control.
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Hu, Bingshan, Feng Chen, Liangliang Han, Huanlong Chen, and Hongliu Yu. "Design and Ground Verification of Space Station Manipulator Control Method for Orbital Replacement Unit Changeout." International Journal of Aerospace Engineering 2018 (September 16, 2018): 1–18. http://dx.doi.org/10.1155/2018/4271035.
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Chinese space station has been in construction phase, and it will be launched around 2020. Lots of orbital replacement units (ORUs) are installed on the space station, and they need to be replaced on orbit by a manipulator. In view of above application requirements, the control method for ORU changeout is designed and verified in this paper. Based on the analysis of the ORU changeout task flow, requirements of space station manipulator’s control algorithms are presented. The open loop path planning algorithm, close loop path planning algorithm based on visual feedback, and impedance control algorithm are researched. To verify the ORU changeout task flow and corresponding control algorithms, a ground experiment platform is designed, which includes a 6-DOF manipulator with a camera and a force/torque sensor, an end effector with clamp/release and screwing function, ORU module, and ORU store. At last, the task flow and control algorithms are verified on the test platform. Through the research, it is found that the ORU changeout task flow designed in this paper is reasonable and feasible, and the control method can be used to control a manipulator to complete the ORU changeout task.
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Shi, Xinxin, and Chenyang Zhu. "Research on Trajectory Planning and Control of Operational Underwater Robots." Mathematical Problems in Engineering 2022 (December 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/1986425.
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For the complex system modeling of operating underwater robots, various modules are built in Simulink to simulate the interaction of various forces and torques inside the underwater vehicle. According to the characteristics of the ROV itself, the ROV dynamics model is reasonably designed. In view of the problems of the position tracking delay, underwater environment interference, and unstable operation of the ROV, the fuzzy fractional PID control algorithm is adopted in the controller in this paper, and the control effect of the fuzzy PID and PID algorithm is compared and analyzed.
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KRAMER, JOSH BROWN, and LUCAS SABALKA. "MULTIDIMENSIONAL ONLINE MOTION PLANNING FOR A SPHERICAL ROBOT." International Journal of Computational Geometry & Applications 20, no. 06 (December 2010): 653–84. http://dx.doi.org/10.1142/s0218195910003475.
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We consider three related problems of robot movement in arbitrary dimensions: coverage, search, and navigation. For each problem, a spherical robot is asked to accomplish a motion-related task in an unknown environment whose geometry is learned by the robot during navigation. The robot is assumed to have tactile and global positioning sensors. We view these problems from the perspective of (non-linear) competitiveness as defined by Gabriely and Rimon. We first show that in 3 dimensions and higher, there is no upper bound on competitiveness: every online algorithm can do arbitrarily badly compared to the optimal. We then modify the problems by assuming a fixed clearance parameter. We are able to give optimally competitive algorithms under this assumption. We show that these modified problems have polynomial competitiveness in the optimal path length, of degree equal to the dimension.
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Liu, Jun Qiang, and Xiao Ling Guan. "Genetic Algorithm with Dynamic Receding Horizon for Airport Capacity Management." Applied Mechanics and Materials 48-49 (February 2011): 561–64. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.561.
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The major goal of air traffic management is to strategically control the flow of airport traffic. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation in airport environment. This paper uses the concept of dynamic receding horizon control (DRHC) to conduct real-time planning for airport capacity management. A gene algorithm is then designed from DRHC point of view. It is shown that DRHC provides a generic and flexible framework for developing real-time allocation algorithms for airport capacity in a dynamic and uncertain environment. Simulation results show that the algorithm is effective and efficient to solve the problem.
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Yubin Wu. "Reference Image Aided Color Matching Design Based on Interactive Genetic Algorithm." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 400–410. http://dx.doi.org/10.52783/jes.1193.
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This paper proposes an interactive genetic algorithm to assist designers in color selection and match different color combinations extracted. Then, based on the CorelDraw development environment, this paper establishes a graphic-based man-machine dialogue model. Through simulation experiments, genetic algorithms such as random selection, hybridization, variation, color region adjustment, etc. Finally, the best color combination is obtained and combined with the image. Within the scope of color extraction value, designers can use interactive genetic algorithm rules to view color planning from a holistic perspective. Under the influence of human-computer interaction, each scheme is optimized step by step, so that the color ideas matching the designer can be found quickly.
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Roy, Shyamal Kumar. "Design and Analysis of P&O algorithm based MPPT solar system in PSIM." YMER Digital 21, no. 06 (June 30, 2022): 1188–94. http://dx.doi.org/10.37896/ymer21.06/b4.
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Conventional solar panels suffer with non-identical losses (resistive losses, diode leakage, loss of material properties etc.). To overcome these unwanted phenomena, a solar maximum power point tracking (MPPT) algorithm has been introduced. In this paper, the algorithms used for tracking the sun (or the solar maximum power point tracking algorithm) has been introduced & a brief description of the basic & easiest algorithm i.e., the P&O Algorithm using PSIM software has been provided to give a clear view about the concept & implementation of MPPT. Keywords: Photovoltaic, MPPT, P&O, PSIM
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Mediavilla, Margarita, José Luis González, Juan Carlos Fraile, and José Ramón Perán. "Reactive approach to on-line path planning for robot manipulators in dynamic environments." Robotica 20, no. 4 (June 24, 2002): 375–84. http://dx.doi.org/10.1017/s0263574702004071.
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This paper describes a new approach to path planning of robot manipulators with many degrees of freedom. It is designed for on-line motion in dynamic and unpredictable environments. The robots react to moving obstacles using a local and reactive algorithm restricted to a subset of its configuration space. The lack of a long-term view of local algorithms (local minima problems) is solved using an off-line pre-planning stage that chooses the subset of the configuration space that minimises the probability of not finding collision free paths. The approach is implemented and tested on a system of three Scorbot-er IX five link robots.
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Chai, Yeyu, Yiting Zhao, and Chenyang Yin. "Indoor Top Mobile Service Robot Based on Improved RRT Algorithm." Journal of Physics: Conference Series 2383, no. 1 (December 1, 2022): 012091. http://dx.doi.org/10.1088/1742-6596/2383/1/012091.
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An indoor top mobile service robot based on the improved RRT algorithm is proposed. The robot is mainly composed of a lifting truss platform and a manipulator, which avoids the mapping and positioning of complex ground and obstacle avoidance planning. At the same time, the kinematics analysis of the manipulator is carried out. In view of the function of obstacle avoidance planning when the manipulator completes the task, the traditional RRT algorithm is improved to overcome the shortcomings of the low efficiency of path planning in the traditional RRT algorithm, and the planned path quality is uncertain and not smooth enough. The simulation environment is built based on ROS platform to verify the feasibility of the algorithm. To a certain extent, it improves the autonomy of robots to complete tasks.
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Ghaleb, Mukhtar, Shamala Subramaniam, and Safwan M. Ghaleb. "An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points." Symmetry 11, no. 11 (October 23, 2019): 1326. http://dx.doi.org/10.3390/sym11111326.
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A recent trend in wireless sensor network (WSN) research is the deployment of a mobile element (ME) for transporting data from sensor nodes to the base station (BS). This helps to achieve significant energy savings as it minimizes the communications required among nodes. However, a major problem is the large data gathering latency. To address this issue, the ME (i.e., vehicle) should visit certain rendezvous points (i.e., nodes) to collect data before it returns to the BS to minimize the data gathering latency. In view of this, we propose a rendezvous-based approach where some certain nodes serve as rendezvous points (RPs). The RPs gather data using data compression techniques from nearby sources (i.e., affiliated nodes) and transfer them to a mobile element when the ME traverses their paths. This minimizes the number of nodes to be visited, thereby reducing data gathering latency. Furthermore, we propose a minimal constrained rendezvous point (MCRP) algorithm, which ensures the aggregated data are relayed to the RPs based on three constraints: (i) bounded relay hop, (ii) the number of affiliation nodes, and (iii) location of the RP. The algorithm is designed to consider the ME’s tour length and the shortest path tree (SPT) jointly. The effectiveness of the algorithm is validated through extensive simulations against four existing algorithms. Results show that the MCRP algorithm outperforms the compared schemes in terms of the ME’s tour length, data gathering latency, and the number of rendezvous nodes. MCRP exhibits a relatively close performance to other algorithms with respect to power algorithms.
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Geffner, Hector. "The Model-Based Approach to Autonomous Behavior: A Personal View." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1709–12. http://dx.doi.org/10.1609/aaai.v24i1.7765.
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The selection of the action to do next is one of the central problems faced by autonomous agents. In AI, three approaches have been used to address this problem: the programming-based approach, where the agent controller is given by the programmer, the learning-based approach, where the controller is induced from experience via a learning algorithm, and the model-based approach, where the controller is derived from a model of the problem. Planning in AI is best conceived as the model-based approach to action selection. The models represent the initial situation, actions, sensors, and goals. The main challenge in planning is computational, as all the models, whether accommodating feedback and uncertainty or not, are intractable in the worst case. In this article, I review some of the models considered in current planning research, the progress achieved in solving these models, and some of the open problems.
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Gao, Mengjing, Tian Yan, Wenxing Fu, Zhenfei Feng, and Hang Guo. "Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV." Drones 8, no. 1 (December 30, 2023): 9. http://dx.doi.org/10.3390/drones8010009.
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In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.
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Gehrung, J., M. Hebel, M. Arens, and U. Stilla. "EFFICIENT TOUR PLANNING FOR A MEASUREMENT VEHICLE BY COMBINING NEXT BEST VIEW AND TRAVELING SALESMAN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 729–36. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-729-2021.
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Abstract. Path planning for a measuring vehicle requires solving two popular problems from computer science, namely the search for the optimal tour and the search for the optimal viewpoint. Combining both problems results in a new variation of the Traveling Salesman Problem, which we refer to as the Explorational Traveling Salesman Problem. The solution to this problem is the optimal tour with a minimum of observations. In this paper, we formulate the basic problem, discuss it in context of the existing literature and present an iterative solution algorithm. We demonstrate how the method can be applied directly to LiDAR data using an occupancy grid. The ability of our algorithm to generate suitably efficient tours is verified based on two synthetic benchmark datasets, utilizing a ground truth determined by an exhaustive search.
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Zhang, Jing, Jun Wu, Xiao Shen, and Yunsong Li. "Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star." International Journal of Advanced Robotic Systems 18, no. 5 (September 1, 2021): 172988142110427. http://dx.doi.org/10.1177/17298814211042730.
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The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.
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Palomeras, Narcís, Marc Carreras, and Juan Andrade-Cetto. "Active SLAM for Autonomous Underwater Exploration." Remote Sensing 11, no. 23 (November 28, 2019): 2827. http://dx.doi.org/10.3390/rs11232827.
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Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.
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Liao, Fayi, Zhi Qiu, and Chuanqing Pang. "Research on Path Planning of Handling Robot in Chinese Medicine Factory." Frontiers in Science and Engineering 3, no. 11 (November 21, 2023): 62–71. http://dx.doi.org/10.54691/fse.v3i11.5711.
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The path planning of handling robot in Chinese medicine factory is the key problem to improve production efficiency and reduce labor cost. In view of the complex layout characteristics in the handling process of Chinese medicine plant, this paper studies and improves the existing path planning algorithm. Firstly, through the analysis of the production environment of the Chinese medicine factory, a suitable map model was established. Then, combined with the operation environment of the handling robot in the Chinese medicine factory, the handling robot has A large number of nodes and many kinds of paths. The improved A* algorithm is used to plan the moving path of the robot. The effectiveness and practicability of the proposed path planning algorithm are verified by Matlab simulation test. Finally, the path conflict problem is solved.
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Chen, Xinxiu, Yannan Chen, Jinyu Xu, and Yongxu Zheng. "SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios." Highlights in Science, Engineering and Technology 52 (July 4, 2023): 33–39. http://dx.doi.org/10.54097/hset.v52i.8722.
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In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.
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Wang, Gang, Hongyuan Wen, Jun Feng, and Jun Zhou. "Motion Trajectory Planning and Design of Material Spraying Service Robot." Advances in Materials Science and Engineering 2022 (August 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/8923901.
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The application of robots reduces repetitive and dangerous tasks for humans, especially the spraying robot, because many spraying materials are corrosive, toxic, and harmful. This paper designs the motion trajectory planning of the material spraying service robot. With the increasing demand for technology, there are strict requirements for the uniformity and thickness of spraying. In view of this, this paper proposes an algorithm for modeling kinematics using a joint screw and optimizes the modeling algorithm using particle swarm optimization. This makes the industrial spraying robot more intelligent and more capable of completing high-standard tasks. The experimental results in this paper show that when the spraying radii on the large-curvature cone surface are 44.5 and 49.5 mm, respectively, the coating distribution in the intersection area of the curved surfaces can be well controlled, and the optimized algorithm can better plan the path of the spraying robot.
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Khan, Muhammad Raza, and Joshua E. Blumenstock. "Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 606–13. http://dx.doi.org/10.1609/aaai.v33i01.3301606.
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With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.
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Chai, Shijia. "Improved value iteration network for path planning." Journal of Physics: Conference Series 2634, no. 1 (November 1, 2023): 012033. http://dx.doi.org/10.1088/1742-6596/2634/1/012033.
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Abstract In this study, a series of improved path planning algorithms are designed for path planning tasks in autonomous control based on deep reinforcement learning. The Value Iteration Network (VIN) is used to deal with the path planning problem. Origin VIN performs well on small size maps, but when it comes to a bigger size of map on test set, the success rate decreased. In order to solve the problem that origin VIN lacks long-distance multi-step planning ability on large maps and generalization ability is insufficient, a three-step improvement was made. First of all, in view of the inconvenient data flow and the disappearance of gradients caused by the network being too deep, the jump connection structure is used to obtain the deeper VIN, in which the accuracy of the experiment is improved. Secondly, with the purpose of solving the problem that the complexity of the model is greatly increased due to the deepening of the network, Batch normalization is used to obtain a new network with dueling architecture plus batch normalization layer, which further accelerates the convergence speed of the network. Third, to deal with the global path planning problem on the big map, the hierarchical network structure is adopted for hierarchical value iteration, and the Hierarchical Structure VIN is obtained. In Hierarchical Structure VIN, the long-term planning ability and generalization ability of the algorithm have been significantly improved, and the algorithm could figure out the large-scale and complex path planning problem.
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Zhang, Ben, Anxi Yu, Xing Chen, Zhengbin Wang, and Zhen Dong. "Comparative Analysis of Single-View and Multi-View Airborne SAR Positioning Error and Course Planning for Multi-View Airborne SAR Optimal Positioning." Remote Sensing 14, no. 13 (June 25, 2022): 3055. http://dx.doi.org/10.3390/rs14133055.
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Airborne synthetic aperture radar (Airborne SAR) can accurately locate key targets and regions by using the flight parameters of the aircraft and the relative position information between the aircraft and the target which can be obtained from the airborne positioning and orientation system (POS). In the course of flight, the aircraft will deviate from the ideal flight path due to atmospheric turbulence, which results in the calculation deviating from the actual target position. In order to improve the target positioning accuracy, it is necessary to study the influence of aircraft motion error on the target positioning error. This study discusses the positioning accuracy of single-view airborne SAR from the perspective of linear Range-Doppler algorithm (RDA), and deduces the multi-view airborne SAR positioning error transfer model based on the multi-view airborne SAR positioning model. Based on these, we analyze the main factors that affect the positioning accuracy of the two positioning methods in detail and quantitatively reveal the mechanism by which the multi-view airborne SAR positioning method can improve the target positioning accuracy compared with the single-view airborne SAR positioning method; we also solve the problem of course planning for multi-view airborne SAR optimal positioning. The research results can provide theoretical support for the analysis of factors influencing positioning error and the positioning error correction of airborne SAR.
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WANG, PENGPENG, RAMESH KRISHNAMURTI, and KAMAL GUPTA. "GENERALIZED WATCHMAN ROUTE PROBLEM WITH DISCRETE VIEW COST." International Journal of Computational Geometry & Applications 20, no. 02 (April 2010): 119–46. http://dx.doi.org/10.1142/s0218195910003232.
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In this paper, we introduce a generalized version of the Watchman Route Problem (WRP) where the objective is to plan a continuous closed route in a polygon (possibly with holes) and a set of discrete viewpoints on the planned route such that every point on the polygon boundary is visible from at least one viewpoint. Each planned viewpoint has some associated cost. The total cost to minimize is a weighted sum of the view cost, proportional to the number of viewpoints, and the travel cost, the total length of the route. We call this problem the Generalized Watchman Route Problem or the GWRP. We tackle a restricted nontrivial (it remains NP-hard and log-inapproximable) version of GWRP where each polygon edge is entirely visible from at least one planned viewpoint. We call it Whole Edge Covering GWRP. The algorithm we propose first constructs a graph that connects O(n12) number of sample viewpoints in the polygon, where n is the number of polygon vertices; and then solves the corresponding View Planning Problem with Combined View and Traveling Cost, using an LP-relaxation based algorithm we introduced in [27, 29]. We show that our algorithm has an approximation ratio in the order of either the view frequency, defined as the maximum number of sample viewpoints that cover a polygon edge, or a polynomial of log n, whichever is smaller.
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Cheng, Dongling. "Water Allocation Optimization and Environmental Planning with Simulated Annealing Algorithms." Mathematical Problems in Engineering 2022 (May 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/2281856.
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Because of the continuous deterioration of water environment, it is ensured that the basic water demand of ecological environment is the key task of water resources utilization and control in China. In view of the uneven distribution of domestic water resources “more in the South and less in the north, more in the East and less in the west,” it is very necessary to optimize the allocation of water resources. This paper aims to optimize the allocation of water resources through simulated annealing algorithm (SAA), hoping to optimize the allocation of water resources through diversion, water intake, and storage measures such as pipelines. Based on this, this paper proposes an improved SAA pipeline construction algorithm. Aiming at the distribution of water sources in the Yangtze River Basin, the algorithm is used to optimize the objective function path to solve the unbalanced problem of rich and lack of regional water resources. And after optimizing the annealing simulation algorithm, the simulation optimization ability is significantly improved. Experiments show that the improved SAA can improve the optimal configuration by more than 50% and up to 96%, indicating that the improved algorithm has a more stable optimization planning function for the optimization of objectives and can often get a more perfect route.
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Zhao, Yao, Zhi Xiong, Shuailin Zhou, Jingqi Wang, Ling Zhang, and Pascual Campoy. "Perception-Aware Planning for Active SLAM in Dynamic Environments." Remote Sensing 14, no. 11 (May 27, 2022): 2584. http://dx.doi.org/10.3390/rs14112584.
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This paper presents a perception-aware path planner for active SLAM in dynamic environments using micro-aerial vehicles (MAV). The “Next-Best-View” planner (NBVP planner) is combined with an active loop closing, which is called the Active Loop Closing Planner (ALCP planner). The planner is proposed to avoid both static and dynamic obstacles in unknown environments while reducing the uncertainty of the SLAM system and further improving the accuracy of localization. First, the receding horizon strategy is adopted to find the next waypoint. The cost function that combines the exploration gain and the loop closing gain is designed. The former can reduce the mapping uncertainty, while the latter takes the loop closing possibility into consideration. Second, a key waypoint selection strategy is designed. The selected key waypoints, instead of all waypoints, are treated as potential loop-closing points to make the algorithm more efficient. Moreover, a fuzzy RRT-based dynamic obstacle avoidance algorithm is adopted to realize obstacle avoidance in dynamic environments. Simulations in different challenging scenarios are conducted to verify the effectiveness of the proposed algorithm.
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Mi, Xifeng. "Extended Function Analysis of Urban Planning and Design Based on Automatic Extraction Algorithm of Closed Area Boundary." Advances in Multimedia 2022 (January 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/1098524.
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With the continuous development of social economy, the expansion of cities often leads to the disorderly utilization of land resources and even waste. In view of these limitations and requirements, this paper introduces the automatic extraction algorithm of closed area boundary, combs the requirements of urban boundary extraction involved in urban planning and design, and uses the technology of geospatial analysis to carry out spatial analysis practice from three angles, so as to realize the expansion of functional analysis of urban planning and design and improve the efficiency and rationality of urban planning. The simulation results show that the automatic extraction algorithm of closed area boundary is effective and can support the functional analysis of urban planning and design expansion.
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Jing, Wei, and Kenji Shimada. "Model-based view planning for building inspection and surveillance using voxel dilation, Medial Objects, and Random-Key Genetic Algorithm." Journal of Computational Design and Engineering 5, no. 3 (December 5, 2017): 337–47. http://dx.doi.org/10.1016/j.jcde.2017.11.013.
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Abstract Model-based view planning is to find a near-optimal set of viewpoints that cover the surface of a target geometric model. It has been applied to many building inspection and surveillance applications with Unmanned Aerial Vehicle (UAV). Previous approaches proposed in the past few decades suffer from several limitations: many of them work exclusively for 2D problems, generate only a sub-optimal set of views for target surfaces in 3D environment, and/or generate a set of views that cover only part of the target surfaces in 3D environment. This paper presents a novel two-step computational method for finding near-optimal views to cover the surface of a target set of buildings using voxel dilation, Medial Objects (MO), and Random-Key Genetic Algorithm (RKGA). In the first step, the proposed method inflates the building surfaces by voxel dilation to define a sub-volume around the buildings. The MO of this sub-volume is then calculated, and candidate viewpoints are sampled using Gaussian sampling around the MO surface. In the second step, an optimization problem is formulated as (partial) Set Covering Problem and solved by searching through the candidate viewpoints using RKGA and greedy search. The performance of the proposed two-step computational method was measured with several computational cases, and the performance was compared with two previously proposed methods: the optimal-scan-zone method and the randomized sampling-based method. The results demonstrate that the proposed method outperforms the previous methods by finding a better solution with fewer viewpoints and higher coverage ratio compared to the previous methods. Highlights A two-step “generate-test” view planning method is proposed. Voxel dilation, Medial Objects and Gaussian sampling are used to generate viewpoints. Random-Key GA and Greedy search are combined to solve the Set Covering Problem. The proposed method is benchmarked and outperforms two existing methods.