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Folding Process Planning of Rigid Origami Using the Explicit Expression and rapidly exploring random tree Method

JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME 2022年第1期14卷 011003页

作者： Guo, Zhen Tachi, Tomohiro Yu, Hongying Harbin Inst Technol Sch Mechatron Engn 92 Xidazhi St Harbin 150001 Heilongjiang Peoples R China Univ Tokyo Dept Gen Syst Studies Meguro Ku 3-8-1 Komaba Tokyo 1538902 Japan

In this study, we propose a novel method for planning the folding process of a rigid origami mechanism, i.e., we explore the intermediate process of the mechanism from an initial state to a target state without self-intersection via a path-finding algorithm. A typical problem associated with a path-finding algorithm is that a feasible configuration space of rigid origami is a lower-dimensional subset of the entire parameter space. When all the folding angles are considered as free parameters to plan the folding process, it is generally not possible to obtain a feasible configuration via sampling. In this study, the parameters corresponding to the degree-of-freedom (DOF) are used as independent variables, and the remaining fold angles are considered as dependent variables that can be calculated via the explicit expression method (EEM). First, we explain the method for choosing the parameters related to DOF to represent the configuration of the origami mechanism. Then, we show the procedure for selecting a valid configuration from many possible configurations computed via EEM. For this purpose, we introduce criteria for each vertex to determine whether the two configurations can be continuously connected. Next, the method for planning the folding process of rigid origami is introduced via the rapidly exploring random tree (RRT) method. Finally, we implemented the folding process simulation platform and applied it to different patterns. The results of the experiments are presented.

关键词： folding and origami folding process planning rapidly exploring random tree degree-of-freedom

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Hybrid Bidirectional rapidly exploring random tree Path Planning Algorithm with Reinforcement Learning

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021年第1期25卷 121-129页

作者： Wang, Junkui Hirota, Kaoru Wu, Xiangdong Dai, Yaping Jia, Zhiyang Beijing Inst Technol Sch Automat 5 Zhongguancun South St Beijing 100081 Peoples R China

The randomness of path generation and slow convergence to the optimal path are two major problems in the current rapidly exploring random tree (RRT) path planning algorithm. Herein, a novel reinforcement-learning-based hybrid bidirectional rapidly exploring random tree (H-BRRT) is presented to solve these problems. To model the random exploration process, a target gravitational strategy is introduced. Reinforcement learning is applied to the improved target gravitational strategy using two operations: random exploration and target gravitational exploration. The algorithm is controlled to switch operations adaptively according to the accumulated performance. It not only improves the search efficiency, but also shortens the generated path after the proposed strategy is applied to a bidirectional rapidly exploring random tree (BRRT). In addition, to solve the problem of the traditional RRT continuously falling into the local optimum, an improved exploration strategy with collision weight is applied to the BRRT. Experimental results implemented in a robot operating system indicate that the proposed H-BRRT significantly outperforms alternative approaches such as the RRT and BRRT. The proposed algorithm enhances the capability of identifying unknown spaces and avoiding local optima.

关键词： path planning reinforcement learning Q-learning rapidly exploring random tree target gravitational strategy

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A Predictive Path Planning Algorithm for Mobile Robot in Dynamic Environments Based on rapidly exploring random tree

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8223-8232页

作者： Zhang, Zhenghao Qiao, Bing Zhao, Wentong Chen, Xi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Room A212 Nanjing Peoples R China

Path planning of mobile robot has always been a focus in the field of robotics for a long time, which is highly related to the ability of the robot to execute tasks. However, many traditional path planning algorithms are not suitable for the dynamic environments, which are more common in reality. In this paper, a predictive path planning algorithm for mobile robot in the dynamic environments is proposed based on the rapidly exploring random tree algorithm. Under the premise of pre-searching the global approximate optimal path, the algorithm introduces a simple movement prediction about the mobile robot itself and the dynamic obstacles according to their velocities and then re-plans the path against the dynamic obstacles, which accelerates the search speed of re-planning, reduces the path cost and the probability of collision, and improves the quality of the re-planning path. Computer simulations show the effectiveness and the superiority of the new strategy.

关键词： Path planning Dynamic environments rapidly exploring random tree Predictive Re-planning

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Application of rapidly exploring random tree Algorithm in the Scene of Road Network with Scheduled Tracks 2

Application of Rapidly Exploring Random Tree Algorithm in th...

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2nd International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Hu Zhenghua Zhong Qiubo Tong Chunya Wang Kuai Ningbo Univ Technol Ningbo 315211 Zhejiang Peoples R China Chongqing Survey Inst Chongqing 401121 Peoples R China

ISBN: (纸本)9781538632604

As the traffic situation of the road network in urban areas is becoming increasingly congested, the classical algorithm of rapidly exploring random tree (RRT) is introduced into the scene of the road network with scheduled tracks in this paper. And a dynamic path planning method based on the RRT algorithm is designed. The algorithm takes the start road segment of the route as the root node in the random tree to perform random extensions until the leaf node of the random tree covers the target road segment. Finally, the algorithm is simulated on the road network with congested areas in Wuhan. The result shows that the proposed method can not only effectively avoid the congestion areas in the road network, but also better meet the real-time demand for dynamic path planning.

关键词： rapidly exploring random tree avoidance of traffic congestion route planning self-adjusting

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Collision-free and dynamically feasible trajectory planning for omnidirectional mobile robots using a novel B-spline based rapidly exploring random tree

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2021年第3期18卷

作者： Sun, Yuxi Zhang, Chengrui Liu, Chang Shandong Univ Sch Mech Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan Peoples R China Shandong Univ Natl Demonstrat Ctr Expt Mech Engn Educ Jinan Peoples R China

Generating a collision-free and dynamically feasible trajectory with a better clearance in a cluttered environment is still a challenge. We propose two dynamically feasible B-spline based rapidly exploring random tree (RRT) approaches, which are named DB-RRT and FMDB-RRT, to achieve path planning and trajectory planning simultaneously for omnidirectional mobile robots. DB-RRT combines the convex hull property of the B-spline and RRT's rapid expansion capability to generate a safe and dynamically feasible trajectory. Firstly, we analyze the tree's sustainable growth ability and put forward the dynamically feasible region. A geometric method is proposed to judge whether finding a dynamically feasible trajectory quickly. Secondly, we design two steer functions to guide the tree's growth, improve efficiency, and decrease the number of iterations. To further increase the clearance and reduce the randomness of the trajectory, we propose FMDB-RRT, which uses the path of fast marching to guide the rapid growth of DB-RRT. Then, assuming that the number of sampled points is sufficient to represent the dynamically feasible region, the DB-RRT is proved to be probabilistically complete. Finally, by conducting experimental comparisons with other algorithms in different environments and deploying the proposed algorithm to an omnidirectional mobile robot, the effectiveness and good performance of the algorithm have been verified.

关键词： Path planning trajectory planning rapidly exploring random tree omnidirectional mobile robots uniform B-spline fast marching method

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Phototropism rapidly exploring random tree: An efficient rapidly exploring random tree approach based on the phototropism of plants

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第5期17卷

作者： Zhuge, Chengchen Liu, Jiayin Guo, Dongyan Cui, Ying Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing 210031 Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

Inspired by the phototropism of plants, a novel variant of the rapidly exploring random tree algorithm as called phototropism rapidly exploring random tree is proposed. The phototropism rapidly exploring random tree algorithm expands less tree nodes during the exploration period and has shorter path length than the original rapidly exploring random tree algorithm. In the algorithm, a virtual light source is set up at the goal point, and a light beam propagation method is adopted on the map to generate a map of light intensity distribution. The phototropism rapidly exploring random tree expands its node toward the space where the light intensity is higher, while the original rapidly exploring random tree expands its node based on the uniform sampling strategy. The performance of the phototropism rapidly exploring random tree is tested in three scenarios which include the simulation environment and the real-world environment. The experimental results show that the proposed phototropism rapidly exploring random tree algorithm has a higher sampling efficiency than the original rapidly exploring random tree, and the path length is close to the optimal solution of the rapidly exploring random tree* algorithm without considering the non-holonomic constraint.

关键词： rapidly exploring random tree phototropism virtual light P-RRT RRT*

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Improved rapidly exploring random tree using salp swarm algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Muhsen, Dena Kadhim Raheem, Firas Abdulrazzaq Sadiq, Ahmed T. Univ Technol Iraq Comp Sci Dept Baghdad 10066 Iraq Univ Technol Iraq Control & Syst Engn Dept Baghdad 10066 Iraq

Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.

关键词： path planning rapidly exploring random tree salp swarm algorithm improved RRT-SSA

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Underground Metro Interstation Horizontal-Alignment Optimization with an Augmented rapidly exploring random-tree Connect Algorithm

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2020年第11期146卷

作者： Yang, Dongying He, Qing Yi, Sirong Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Minist Educ Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China SUNY Buffalo Dept Ind & Syst Engn Buffalo NY 14620 USA SUNY Buffalo Dept Civil Struct & Environm Engn Buffalo NY 14620 USA

The design of an underground metro alignment in an urban area is complex and challenging. The metro alignment constantly invades districts with high-density buildings and specially planned districts, which results in large additional expenses due to reconstruction, land requisition, demolition, vibration absorption, and environmental protection. This paper presents a method for the optimization of the horizontal alignment between metro stations to minimize the overall cost. The proposed method includes three steps: (1) low-cost path search, (2) initial horizontal alignment generation, and (3) horizontal alignment optimization. First, an augmented rapidly exploring random-tree connect (RRT-connect) algorithm is developed to search for a low-cost path, in which the buffer zone of the low-cost path defines the corridor constraint of the horizontal alignment optimization. Subsequently, based on the low-cost path, an initial horizontal alignment is generated that determines the number of design variables, i.e., intersection points. Next, within the corridor constraint, the method optimizes the intersection point coordinates and curve radius on the initial horizontal alignment using a differential evolution (DE) algorithm. A real-world metro line was studied to verify the effectiveness of the proposed optimization approach. (c) 2020 American Society of Civil Engineers.

关键词： Metro alignment optimization rapidly exploring random tree Differential Evolution algorithm Geographic information systems

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Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第5期236卷 883-896页

作者： Liu, Wei Jing, Cheng Wan, Ping Ma, Yongheng Cheng, Jin Yancheng Inst Technol Sch Automot Engn Yancheng 224051 Jiangsu Peoples R China Jiangsu Coastal Inst New Energy Vehicle Yancheng Peoples R China

Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.

关键词： Autonomous navigation narrow indoor environments sampling-based algorithms rapidly exploring random tree extended Kalman filtering

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A fusion framework with spatial perception and energy-aware strategies for AUV path planning in uncharted marine environments

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OCEAN ENGINEERING 2025年 328卷

作者： Meng, Wenlong Pu, Yanbo Yu, Hang Gong, Ya Chu, Dianhui Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Shandong Univ Marine Coll Weihai 264209 Peoples R China

Autonomous underwater vehicles (AUVs) play a crucial role in enhancing the exploration and comprehension of marine ecosystems. The ability of AUVs to efficiently plan energy-conserving paths, particularly in uncharted marine environments, is a crucial prerequisite for the successful execution of complex oceanic missions. However, current path-planning algorithms, which depend on constrained underwater sensing technology, frequently face considerable difficulties in optimizing exploration efficiency and producing high-quality trajectories. In this paper, we present a fusion framework designed to efficiently generate energy-conserving paths under conditions of limited information. In depth, considering the uncertainties of the marine environment, we adopt hybrid local-global planning techniques to refactor the single-instance path planning process into a series of iterative local planning steps. In the domain of global path planning, we employ the incremental approach utilizing sliding windows to determine waypoints, thereby refining the computation of local paths and augmenting overall accuracy. At the local path planning level, we incorporated ocean current fields into a modified bidirectional RRT (bi-RRT) algorithm, termed circle-bi-RRT*. By meticulously adjusting the Euclidean extension weight associated with the current fields, our method ensures energy-efficient and collision-free navigation during the local planning stage. After conducting extensive simulations, we rigorously assessed our algorithm's performance in uncharted environments influenced by ocean currents and performed comparative evaluations against advanced related algorithms.

关键词： Energy-efficient path planning Autonomous underwater vehicle Unmapped marine environments Ocean currents rapidly exploring random tree

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