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Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

IEEE ACCESS 2020年 8卷 197407-197419页

作者： Liu, Haishi Chen, Qiyong Pan, Nan Sun, Yuxuan Yang, Yaluo Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Mat Sci & Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Yunnan Police Officer Acad Fac Publ Secur Kunming 650223 Yunnan Peoples R China

Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the rapidly exploring random tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.

关键词： Planning Task analysis Particle swarm optimization Trajectory planning Optimization Drones Collaborative work particle swarm optimization artificial intelligence heterogeneous multi-UAV life-cycle swarm optimization algorithm rapidly exploring random tree

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Efficient Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

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JOURNAL OF MECHANICAL DESIGN 2022年第4期144卷 041703-041703页

作者： Jiang, Chen Liu, Yixuan Mourelatos, Zissimos P. Gorsich, David Fu, Yan Hu, Zhen Univ Michigan Dept Ind & Mfg Syst Engn 2340 Heinz Prechter Engn Complex Dearborn MI 48128 USA Oakland Univ Ctr Engn Dept Mech Engn Room 402D115 Lib Dr Rochester MI 48309 USA US Army Combat Capabil Dev Command Ground Vehicle Syst Ctr 6501 East11 Mile Rd Warren MI 48397 USA Ford Motor Co Strategy & Enterprise Analyt Global Data Insights & Analyt 22001 Michigan Ave Dearborn MI 48128 USA

Reliability-based mission planning of off-road autonomous ground vehicles (AGVs) aims to identify an optimal path under uncertain and deformable terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The repeated evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has a similar computational complexity compared to deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly exploring random tree (RRT*) algorithm allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study with two scenarios verify the accuracy and efficiency of the proposed algorithm.

关键词： mission mobility reliability off-road autonomous ground vehicle outcrossing rate reliability-based mission planning rapidly exploring random tree uncertain environment mobile robots reliability in design simulation-based design uncertainty analysis uncertainty modeling

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A 3D-RRT-star algorithm for optimizing constrained mountain railway alignments

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Pu, Hao Wan, Xinjie Song, Taoran Schonfeld, Paul Peng, Lihui Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Natl Engn Res Ctr High speed Railway Construct Tec Changsha 410075 Peoples R China Univ British Columbia Dept Civil Engn Vancouver BC V6T 1Z4 Canada Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA China Railway Siyuan Survey & Design Grp Co Ltd Wuhan 430063 Peoples R China

Railway alignment development in mountainous regions is a complex problem, especially when there are numerous obstacles in the study area. Obtaining a feasible solution that satisfies all the obstacle constraints requires considerable computing resources and time. To solve this problem, a graph-based shortest path method, i.e., three-dimensional rapidly exploring random tree star (3D-RRT-star), is customized. Three main innovations are included in this method: (1) A heuristic sampling process is proposed to avoid getting the RRT search trapped into local ranges and overlooking possible path alternatives by combining a specially designed RRT node sampling method and a railway spatially-reachable analysis. (2) A multi-level constraint discretization approach is proposed to describe the obstacles in the study area, while procedures are developed to tackle the obstacle constraints dynamically during the search process. (3) An evolutionary search method integrating a sampling strategy and constraint handling operator is devised for generating a set of dissimilar RRT paths, which are finally refined into railway alignments satisfying curve constraints. Ultimately, the proposed method is applied to a realistic railway case. The experimental results confirm that it can yield a better alignment than the best manually obtained solution. Furthermore, its search efficiency is compared to a previous optimization method for this problem and the results reveal significant improvement.

关键词： Railway design Alignment optimization rapidly exploring random tree Heuristic sampling process Evolutionary search Constrained optimization

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Efficient Path Planning for a Microrobot Passing through Environments with Narrow Passages

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MICROMACHINES 2022年第11期13卷 1935-1935页

作者： Huang, Cheng-Ming Hsu, Shu-Hsien Natl Taipei Univ Technol Dept Elect Engn Taipei 106 Taiwan

This paper presents an efficient path-planning algorithm for microrobots attempting to pass through environments with narrow passages. Because of the extremely small size of a microrobot, it is suitable for work in this kind of environment. The rapidly exploring random tree (RRT) algorithm, which uses random sampling points, can quickly explore an entire environment and generate a sub-optimal path for a robot to pass through it;however, the RRT algorithm, when used to plan a path for a microrobot passing through an environment with narrow passages, has the problem of being easily limited to local solutions when it confronts with a narrow passage and is unable to find the final path through it. In light of this, the objectives of the considered path planning problem involve detecting the narrow passages, leading the path toward an approaching narrow passage, passing through a narrow passage, and extending the path search more efficiently. A methodology was proposed based on the bidirectional RRT in which image processing is used to mark narrow passages and their entrances and exits so that the bidirectional RRT can be quickly guided to them and combined with the deterministic algorithm to find paths through them. We designed the methodology such that RRT generates the sampling points for path growth. The multiple importance sampling technique is incorporated with bidirectional RRT, named MIS-BiRRT, to make the path grow faster toward the target point and narrow passages while avoiding obstacles. The proposed algorithm also considers multiple candidate paths simultaneously to expand the search range and then retain the best one as a part of the planning path. After validation from simulation, the proposed algorithm was found to generate efficient path planning results for microrobots to pass through narrow passages.

关键词： microrobot rapidly exploring random tree path planning

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Improving the performance of RRT path planning of excavators by embedding heuristic rules

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Langari, Seied Mohammad Vahdatikhaki, Faridaddin Hammad, Amin Concordia Univ Fac Engn & Comp Sci Montreal PQ Canada Univ Twente Dept Construct Management & Engn Enschede Netherlands

Improving the safety and productivity of earthwork operations is of paramount importance, especially in congested sites where collisions are more probable. Real-time Location Systems and Automated Machine Guidance and Control are expected to improve both the safety and productivity of earthwork operations by providing excavator operators with a higher level of support regarding the path planning of excavators based on site conditions. However, in spite of the large number of studies related to automated path planning of excavators using well-established algorithms from robotics, such as rapidly-exploring random trees and Probabilistic Roadmaps, these studies do not fully consider the engineering constraints of the equipment and do not result in smooth and optimal paths that can be applied in practice. This paper aims to develop a more practical algorithm for the path planning of excavators by embedding heuristic rules and engineering constraints specific to excavators. The proposed method is implemented and tested in a game engine environment. The efficiency of the proposed method in generating a collision-free path, which is expected to result in improved productivity, is validated both quantitatively and visually. The comparative results with other recent and modified versions of the RRT algorithm show that the proposed algorithm is able to find a more realistic path in a shorter time.

关键词： Earthwork Excavator Path Planning rapidly exploring random tree

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Efficient path planning for AUVs in unmapped marine environments using a hybrid local-global strategy

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OCEAN ENGINEERING 2023年第Part2期288卷

作者： Meng, Wenlong Gong, Ya Xu, Fan Tao, Pingping Bo, Pengbo Xin, Shiqing Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Shandong Univ Marine Coll Weihai 264209 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China

The ability of autonomous undersea vehicles (AUVs) to plan paths in unknown marine environments is the precondition for executing complicated missions. However, existing path planning algorithms based on underwater sensing equipment often struggle to achieve efficient exploration and generate high-quality trajectories. In this paper, we introduce a novel approach to efficiently handle the challenge of AUV navigation under limited information. Our solution combines global and local planning techniques to generate optimized paths that guarantee collision-free and efficient operations. In global path planning, we incrementally use the rolling windows to make decisions on high-level path branching while utilizing waypoints from selected branches to refine the calculation of local paths for enhanced accuracy. We employ an efficient small-scale path search strategy at the local path computation level by leveraging sensor-detected environments. In this stage, we propose an advanced rapidly exploring random tree (RRT) algorithm called circle-RRT. By combining adaptive circle sampling with dynamic step sizes, this algorithm can significantly reduce the generation of redundant sampling points and improve the efficiency of local path planning. We evaluated the efficiency of our algorithm in unknown environments through simulations and compared it with previous leading methods.

关键词： Path planning Autonomous undersea vehicle Unmapped obstacle environment rapidly exploring random tree Dynamic step

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tree-based search of the next best view/state for three-dimensional object reconstruction

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第1期15卷

作者： Irving Vasquez-Gomez, J. Enrique Sucar, L. Murrieta-Cid, Rafael Herrera-Lozada, Juan-Carlos Inst Politecn Nacl Consejo Nacl Ciencia & Tecnol Ctr Innovac & Desarrollo Tecnol Comp Mexico City DF Mexico Inst Nacl Astrofis Opt & Electr Puebla Mexico Ctr Invest Matemat Guanajuato Mexico Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Comp Mexico City DF Mexico

Three-dimensional models from real objects have many applications in robotics. To automatically build a three-dimensional model from an object, it is essential to determine where to place the range sensor in order to completely observe the object. However, the view (position and orientation) of the sensor is not sufficient, given that its corresponding robot state needs to be calculated. Additionally, a collision-free trajectory to reach that state is required. In this article, we directly find the state of the robot whose corresponding sensor view observes the object. This method does not require to calculate the inverse kinematics of the robot. Unlike previous approaches, the proposed method guides the search with a tree structure based on a rapidly exploring random tree overcoming previous sampling techniques. In addition, we propose an information metric that improves the reconstruction performance of previous information metrics.

关键词： Next best view path planning 3-D reconstruction rapidly exploring random tree 3-D mapping

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Collaborative Motion Planning for Dual Robotic Arms Based on Split Sampling Space RRT

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UNMANNED SYSTEMS 2024年第6期12卷 973-984页

作者： Gong, Xiangrong Duan, Jianguo Zhang, Qinglei Zhou, Ying Qin, Jiyun Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China

Aiming at the problems of strong sampling randomness, difficulty in collision detection and rough paths of rapidly exploring random tree (RRT) algorithm in collaborative motion planning of dual robotic arms, we propose an RRT algorithm based on split sampling space. First, a split sampling space strategy is proposed. According to the sampling points having a fixed range in a certain axis degree, the step size of random tree generation is restricted to the respective sampling space, and combined with the hierarchical wraparound box method to achieve effective collision detection. Besides, a greedy strategy is used to speed up the growth of random trees in the respective space. Finally, the trajectory smoothing of the dual robotic arms path using the Bezier curve improves the trajectory quality while ensuring that the dual robotic arms will not collide. The feasibility and effectiveness of the algorithm are verified through simulation experiments as well as real UR experiments.

关键词： Dual robotic arms rapidly exploring random tree split sampling space collaborative path planning

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Development of a Mobile Robot for Distribution of Medicine in Hospitals

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IFAC-PapersOnLine 2024年第3期58卷 221-225页

作者： Petko Stoev Rosen Ficherov Momchil Georgiev Institute of Information and Communication Technologies at Bulgarian Academy of Sciences Acad. G Bonchev str. 2 1113-Sofia Bulgaria Technical University of Sofia 8 Kliment Ohridski blvd 1000-Sofia Bulgaria Space Research and Technology Institute at Bulgarian Academy of Sciences Acad. G. Bonchev str. 1 1113-Sofia Bulgaria

Mobile robots are revolutionizing healthcare with their potential to streamline logistics, patient care, and facility maintenance in hospitals. This paper introduces a mobile robot designed for medicine distribution in patient rooms, detailing its mechanical design and working principle. A scenario demonstrating robot path planning for medicine distribution is presented. Future research directions are outlined.

关键词： Mobile Robots Path Planning Healthcare Robotics rapidly exploring random tree RRT Monte-Carlo simulation

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