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IEEE International Conference on Information and Automation 2015

作者： Xiong, Jing Li, Xia Gan, Yangzhou Xia, Zeyang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Xiangtan Univ Inst Control Engn Xiangtan Hunan Peoples R China

ISBN: (纸本)9781467391047

Path planning is one of the most important parts in the research on flexible needle insertion. Based on the Improved rapidly-exploring random tree (I-RRT), the path planning for flexible needle insertion is studied in this paper. Traditional RRT algorithm can generate paths which can avoid obstacle and reach the target precisely, but the path is not continuously differentiable and cannot match the physical constraints of the flexible needle inserting into soft tissue. Aiming at solving this problem, the I-RRT algorithm is proposed to generate paths for minimally invasive treatment and meanwhile guarantee the continuity and effectiveness of the path. The path evaluation function is designed to implement path selection based on path length, obstacle avoidance and the number of arcs. Then the simulation of the flexible needle path is performed in the environment with obstacles. Results show that the I-RRT is more practical and effective for flexible needle insertion path planning.

关键词： Path planning Flexible needle rapidly-exploring random tree

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UAV path planning based on Improved rapidly -exploring random tree 30

UAV path planning based on Improved Rapidly -exploring Rando...

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30th Chinese Control and Decision Conference (CCDC)

作者： Sun Qinpeng Li Meng Wang Tianhe Zhao Chenpeng Univ Jinan Coll Elect Engn Jinan 250022 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

ISBN: (纸本)9781538612446

The RRT algorithm can be used for path planning of Unmanned Aerial Vehicle (UAV). However, the efficiency and success rate of the basic RRT algorithm for path planning need to be improved. In view of this problem, this paper proposed an improved RRT algorithm which based on the dynamic p9 value and the dynamic step length, and bidirectional RRT (Bi-RRT) is combined. The effectiveness and advance of the improved RRT algorithm are verified by simulation experiment.

关键词： Unmanned Aerial Vehicle rapidly-exploring random tree Path planning

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AN ADAPTIVE rapidly-exploring random tree ALGORITHM FOR ASSEMBLY PATH PLANNING IN COMPLEX ENVIRONMENTS

AN ADAPTIVE RAPIDLY-EXPLORING RANDOM TREE ALGORITHM FOR ASSE...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC/CIE)

作者： Shang, Wei Liu, Jian-hua Beijing Inst Technol Sch Mech Engn Beijing 10081 Peoples R China

ISBN: (纸本)9780791854792

We present a refined rapidly-exploring random tree (RRT) algorithm for assembly path planning in complex environments. This algorithm adapts its expansion automatically to explore complex environments with narrow passages and cluttered obstacles more efficiently. In this algorithm, the nodes in the tree are classified by various criterions and different extending values are assigned on them indicating the nearby environment and are used to control the future expansion. A series of tree extending schemes are designed and selectively used based on the attributes of the node and the extending result in each step. We show that the algorithm becomes greedy in constrained environments and promising nodes have higher priority to extend than the non-promising ones. The algorithm is evaluated and applied in assembly path planning. The results show significant performance improvement over the standard RRT planner.

关键词： assembly path planning motion planning rapidly-exploring random tree virtual prototype

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Path planning in dynamic environments based on the improved rapidly-exploring random tree and dynamic window approach 36

Path planning in dynamic environments based on the improved ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Wang, Caiqi Xiong, Yi Deng, Yilong Wu, Qi Wu, Shiqian Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Path planning in dynamic environments holds paramount significance within the realm of mobile robotics. The rapidly-exploring random tree (RRT) algorithm stands as one of the most extensively employed path planning algorithms, distinguished by its probabilistic completeness. However, the algorithm suffers from the long time to obtain the initial solution and the blindness of the expansion. To address these shortcomings, an improved RRT algorithm based on dynamic goal-biased sampling method (Dyn-RRT) is proposed to enhance the algorithm's orientation and reduce the time obtaining the initial solution. To bolster the algorithm's utility in dynamic settings, Dynamic Window Approach (DWA) is integrated into the Dyn-RRT algorithm to enhance adaptability to the dynamic milieu. In addition, simulation experiments show that proposed dynamic goal-biased sampling method is also applicable to other RRT series algorithms and can greatly reduce the time obtaining the initial solution.

关键词： Path planning rapidly-exploring random tree Dynamic window approach Dynamic goal-biased sampling method

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Planning to find an unpredictable evader using rapidly-exploring random tree

Planning to find an unpredictable evader using rapidly-explo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： AlDahak, A Elnagar, A Univ Sharjah Dept Comp Sci Intelligent Syst & Infrastruct Grp Sharjah U Arab Emirates

ISBN: (纸本)0780385667

We are proposing an efficient algorithm for a single pursuer searching for an unpredictable static evader in a 2D environment. The algorithm uses a newly emerging data structure, which is the rapidly-exploring random tree. This data structure is constructed randomly but evenly distributed to cover the whole workspace. We propose the region-based sampling strategy to insure the full uniform coverage of the workspace. During construction of the tree, we keep checking for the evader;which is the stopping criteria of the algorithm. However, if the algorithm reaches the anticipated coverage percentile without detecting the evader, then the algorithm reports failure (i.e., evader is blocked). A lazy collision detection approach is used to resolve any collision with the obstacles during runtime. Simulation results demonstrate the validity of the algorithm.

关键词： pursuit-evasion rapidly-exploring random tree region-based sampling collision detection

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Autonomous Mobile Robot Exploration in Unknown Indoor Environments Based on rapidly-exploring random tree

Autonomous Mobile Robot Exploration in Unknown Indoor Enviro...

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IEEE International Conference on Industrial Technology (IEEE ICIT)

作者： Wu, Cheng-Yan Lin, Huei-Yung Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Dept Elect Engn Chiayi 621 Taiwan

ISBN: (纸本)9781538663769

In recent years, robots have been quickly integrated into people's daily lives. To allow the robots to navigate autonomously, accurate maps have to be provided. Therefore, it is important to make robots to obtain maps automatically and improve the efficiency of autonomous exploration. In this paper, we propose a method based on the rapidly-exploring random tree (RRT) and frontier 2D-SLAM exploration techniques. The proposed system is divided into three parts. First, we construct an initial map with laser range data, and use RRT and frontier detector to identify the frontier points of the initial map. The frontier points are then filtered and clustered to reduce the total number and the computation load. Finally, the score of each frontier point is calculated and the mobile robot is directed to the unknown areas until the map is constructed. In the experiments, the performance is evaluated in various synthetic scenes and real indoor environments. The results show that our system is able to successfully complete the autonomous exploration task in a reasonable time.

关键词： SLAM autonomous exploration rapidly-exploring random tree

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Smooth path planning under maximum curvature constraints for autonomous underwater vehicles based on rapidly-exploring random tree star with B-spline curves

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Feng, Haobo Hu, Qiao Zhao, Zhenyi Feng, Xinglong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China

In recent decades, rapidly-exploring random tree star (RRT*) has garnered significant attention in the field of path planning due to its asymptotical optimality feature. However, the paths obtained by RRT* are comprised of polylines and too tortuous to be followed by underwater robots. To solve the drawback, this paper proposes a novel autonomous underwater vehicle (AUV) path planning method based on B-spline RRT* (BSRRT*). It focuses on planning optimal paths under maximum curvature constraints, which considerably improves the path smoothness. Different from conventional RRT*-based methods, the tree generated by BSRRT* is composed of piecewise B-spline curves that meet the curvature constraint. The analytical formulas of curve curvature and curve length enable BSRRT* to extend the tree with a low computational cost. Furthermore, start and end orientations constraints are imposed via the introduction of start node pairs and end node pairs. BSRRT* also combines with the expanded candidate strategy and the goal-biased strategy for a faster convergence rate. Simulation results demonstrate that compared to existing approaches, BSRRT* can provide shorter smooth paths with lower time costs.

关键词： Path planning rapidly-exploring random tree B-spline curve Orientation node pair strategy Expanded candidate strategy Goal-biased strategy

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Path Planning Algorithm Using the Hybridization of the rapidly-exploring random tree and Ant Colony Systems

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IEEE ACCESS 2021年 9卷 153599-153615页

作者： Pohan, Muhammad Aria Rajasa Trilaksono, Bambang Riyanto Santosa, Sigit Puji Rohman, Arief Syaichu Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia Inst Teknol Bandung Univ Ctr Excellence Artificial Intelligence Vis N Bandung 40132 Indonesia Inst Teknol Bandung Fac Mech & Aerosp Engn Bandung 40132 Indonesia

This paper proposes a path planning algorithm using the hybridization of the rapidly-exploring random tree (RRT) and ant colony system (ACS) algorithms. The RRT algorithm can quickly generate paths. However, the resulting path is suboptimal. Meanwhile, the ACS algorithm can generate the optimal path from the suboptimal previous path information. Then, the proposed algorithm will combine the advantages of RRT with the ACS algorithm. Therefore, it can reach the optimal value with a good convergence speed. We call this proposed algorithm the RRT-ACS algorithm. This study developed a new method for hybridizing the RRT and ACS algorithms for path planning problems. This hybridization process is carried out using one of the ACS principles: the pseudorandom proportional rule. The performance of the proposed algorithm with the RRT*, informed RRT*, RRT*-connect, and informed RRT*-connect algorithms is tested with several benchmark cases. The test results from benchmark case tests with known optimal values indicate that the proposed algorithm has succeeded in achieving those optimal values. Furthermore, statistical tests have also been carried out to verify whether there is a significant difference in performance between the RRT-ACS algorithm and the existing algorithms. The test and statistical analysis results show that the RRT-ACS algorithm has good performance and convergence speed. We also discuss the stability, robustness, convergence, and rapidity of the RRT-ACS algorithm. The results indicates that the RRT-ACS algorithm may be used in applications that require fast and optimal path planning algorithms such as robots and autonomous vehicles.

关键词： Path planning rapidly-exploring random tree ant colony system convergence speed

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An adaptive bidirectional quick optimal rapidly-exploring random tree algorithm for path planning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Huang, Zhuo Gao, Yang Guo, Jian Qian, Chen Chen, Qingwei Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In recent decades, sampling -based algorithms have played an important role in path planning. As an extended algorithm of rapidly -exploring random tree(RRT), the optimal RRT(RRT*) has advantages in asymptotic optimality. However, it still suffers from a slow convergence rate and expensive initial solution heavily. Besides, it cannot provide an effective path planning solution when dealing with narrow passages. To overcome this problem, an adaptive bidirectional quick RRT*(ABQ-RRT*) method is proposed for Unmanned Aerial Vehicle(UAV) in narrow passages. Firstly, the cost function has been redefined by taking into account both the Euclidean distance and the turning angle of the UAV. Secondly, the number of failures in the collision detection is considered and an adaptive goal -biased sampling strategy is adopted to obtain higher -quality sampling points. Thirdly, by local sampling near the obstacles, the type of the local environment is quickly judged. Also, the repulsion field is superimposed to effectively avoid obstacles. Then, during the process of selecting the optimal parent node from the set of candidate nodes, the sorted possible parents consider both the neighborhood nodes and their ancestors to obtain better paths. At last, the effectiveness of ABQ-RRT* is confirmed by comparing it with several other improved RRT algorithms through simulations.

关键词： rapidly-exploring random tree Sampling-based algorithms Narrow passage Path planning

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Recent advances in rapidly-exploring random tree: A review

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HELIYON 2024年第11期10卷 e32451页

作者： Xu, Tong Jiangsu Open Univ Sch Informat Technol Nanjing 210000 Peoples R China

Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the rapidly-exploring random tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRTbased improved algorithms still suffer from the problems of difficult to design the hyperparameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot colla

关键词： rapidly-exploring random tree Branching strategy improvement Sampling strategy improvement Post-processing Model driven Robot

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