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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Zhang, Hao Lyu, Guangqiang Xie, Yanjing Dong, Fangyan Chen, Kewei Faculty of Mechanical Engineering and Mechanics Ningbo University Ningbo315211 China Zhejiang University Ningbo Institute of Technology Ningbo315000 China Ningbo Guangqiang Robot Technology Co. Ltd. Ningbo315100 China

ISBN: (纸本)9789819647552

In order to improve the robot’s exploration efficiency of the unknown environment, this paper proposes a robot autonomous exploration algorithm based on local restricted sampling and frontier priority classification, which makes the local tree "short-sighted" by restricting the sampling range of the local tree, so that the robot can focus on exploring the vicinity of the direction of travel, avoiding repeated sampling of the explored area and improving the detection efficiency. This avoids repeated sampling of the explored area, improves the detection efficiency, and avoids frequent steering of the robot. Based on the above sampling method, by prioritizing the classification of the frontiers detected by the local tree and the global tree, the priority of the frontiers detected by the local tree is increased to ensure that the robot gives priority to exploring the frontier region detected by the local tree, and then explores the frontier detected by the global tree when the local tree fails to detect the frontier, which ensures that the unknown environment in the vicinity of the robot is sufficiently explored before exploring the more distant unknown environment. Through experimental studies in different typical indoor scenes, the results show that the algorithm proposed in this paper can effectively avoid the backtracking problem of the robot, shorten the exploration time and path, and improve the exploration efficiency, which verify the effectiveness of the algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： autonomous robot exploration frontier prioritization classification local constrained sampling rapidly-exploring random tree

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Mobile Robot Exploration Based on rapidly-exploring random trees and Dynamic Window Approach 5

Mobile Robot Exploration Based on Rapidly-exploring Random T...

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5th International Conference on Control, Automation and Robotics (ICCAR)

作者： Zeng, Taiping Si, Bailu Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781728133263

Exploration is a critical function for autonomous mobile robots. Traditionally, the entire map has to be processed to extract frontiers and perform path planning. However, as the robot explores the environment, the map grows over time, and increasing computational resources are required, especially for large-scale environments. Moreover, only a few methods focus on the exploration on point cloud maps. Here, I present a new practical method to autonomous mobile robot exploration based on a sparse, relatively small-size point cloud local map, which combines rapidly-exploring random tree (RRT) and dynamic window approach (DWA) algorithm together. The local map is built from the consecutive inputs of raw point clouds using an inexpensive 3D sensor, i.e. Kinect V2. Frontiers are effectively detected and local path planning is performed by RRT algorithm directly on unordered point cloud local maps. Motion planning is performed online by DWA to avoid obstacles and direct a nonholonomic mobile robot towards frontiers separating known environments from unknown environments. Embedded with simultaneous localization and mapping (SLAM) system of my previous research, the performance of the proposed method is evaluated in a large-scale customized virtual environment with a size of 33 x 29 x 6m using Gazebo as the robotic simulator. The results suggest that the proposed algorithm can accurately direct the nonholonomic mobile robot to unexplored environments in real time. Also, it successfully helps build a coherent semi metric topological map. The proposed algorithm shows great efficient performance and is suitable for both static and dynamic environments. Combination of RRT and DWA algorithm on point clouds, as a general approach, can be extended to generic 3D nonplanar physical environments. Videos of the experiments can be found at https://***0i766fhs9Ds.

关键词： mobile robots autonomous exploration pointclouds motion planning rapidly-exploring random tree dynamicwindow approach

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Scene Context-aware rapidly-exploring random trees for Global Path Planning

Scene Context-aware Rapidly-exploring Random Trees for Globa...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9781538691519

This paper introduces a global path planning method for autonomous systems. Global path planning finds a feasible and collision-free path in an environment in which various kinds of regions and objects exist. However, the most planning methods use information such as collision-free space and obstacles in the environment. Interactions at each region (e.g., sidewalk and pavement) would be different. In this paper, we propose a method for global path planning taking semantic scene context into account. In contrast to conventional path planning methods which use collision-free and obstacle regions, the proposed method represents an environment as a cost map. The cost map is estimated from demonstrated human behaviors and feature maps derived from semantic scene context. To find a path on the cost map, we define a path cost and leverage an optimal rapidly-exploring random tree (RRT*) algorithm. We evaluate the proposed method regarding accuracy and computational efficiency with two public datasets and our contributed dataset. Experimental results show that our method successfully reproduces paths like human behaviors in short computational time.

关键词： path planning rapidly-exploring random tree semantic scene context

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Computational Path Planner for Product Assembly in Complex Environments

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Chinese Journal of Mechanical Engineering 2013年第2期26卷 282-292页

作者： SHANG Wei LIU Jianhua NING Ruxin LIU Mi School of Mechanical Engineering Beijing Institute of Technology

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree （RRT） algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

关键词： assembly path planning motion planning stochastic collision detection rapidly-exploring random tree adaptive RRT

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Adaptive Caging Configuration Design Algorithm of Hyper-Redundant Manipulator for Dysfunctional Satellite Pre-Capture

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IEEE ACCESS 2020年 8卷 22546-22559页

作者： Wan, Wenya Sun, Chong Yuan, Jianping Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam Xian 710072 Peoples R China

This paper presents an adaptive caging configuration design algorithm of the hyper-redundant manipulator for dysfunctional satellite pre-capture. Taking advantages of the extreme flexibility and hyper-redundancy, the hyper-redundant manipulator wraps its whole body around the dysfunctional satellite to restrain its motion without requiring grappling points and accurate information. However, the hyper-redundancy also makes the caging configuration design more complicated and challenging. In this paper, the dynamic sequential caging following algorithm based on rapidly-exploring random tree algorithm is proposed to search the caging configuration in real-time. First, according to the discretized caging trace, which is twining around the grasped object and selected based on the caging conditions, the joints of the hyper-redundant manipulator are divided into several groups in advance. Then, the joint angles are searched group by group to realize the match of the discretized caging trace by the hyper-redundant manipulator. As a result, the configuration between the grasped object and the hyper-redundant manipulator satisfies the caging conditions. The main advantages of the proposed caging motion planning algorithm lie in the avoidance of the inversion and the efficiency of computation. Finally, the pre-capture of two dysfunctional satellites with different shapes using a twenty universal joint manipulator is implemented, and the simulation results verify the efficiency of the proposed method.

关键词： Caging configuration design hyper-redundant manipulator caging trace dynamic sequential caging following algorithm rapidly-exploring random tree

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Efficient Robot Motion Planning Using Bidirectional-Unidirectional RRT Extend Function

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第3期19卷 1859-1868页

作者： Wang, Jiankun Chi, Wenzheng Li, Chenming Meng, Max Q. -H. Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Soochow Univ Sch Mech & Elect Engn Robot & Microsyst Ctr Suzhou 215021 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Res Inst Shenzhen 518057 Peoples R China

In this article, based on the rapidly-exploring random tree (RRT), we propose a novel and efficient motion planning algorithm using bidirectional RRT search. First, a RRT extend function is used to organize the sampled states under kinodynamic constraints. Meanwhile, the bidirectional search strategy is implemented to grow a forward tree and backward tree simultaneously in the tree extension process. When these two trees meet each other, the backward tree will act as a heuristic to guide the forward tree to continuously grow toward the goal state, where the algorithm switches to unidirectional search mode. Therefore, the two-point boundary value problem (BVP) in the connection process is avoided, and the extension process gets much accelerated. We also prove that probabilistic completeness is guaranteed. Numerical simulations are conducted to demonstrate that the proposed algorithm performs much better than the state-of-the-art algorithms in different environments.

关键词： Planning Heuristic algorithms Robot motion Search problems Robots Trajectory Robot kinematics Robotics robot motion planning rapidly-exploring random tree bidirectional search

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Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第6期16卷 3026-3035页

作者： Taheri, Ehsan Ferdowsi, Mohammad Hossein Danesh, Mohammad Malek Ashtar Univ Technol Elect Engn Dept Control Grp Tehran 158751774 Iran Isfahan Univ Technol Dept Mech Engn Esfahan *** Iran

A randomized sampling-based path planning algorithm for holonomic mobile robots in complex configuration spaces is proposed in this article. A complex configuration space for path planning algorithms may cause different environmental constraints including the convex/concave obstacles, narrow passages, maze-like spaces and cluttered obstacles. The number of vertices and edges of a search tree for path planning in these configuration spaces would increase through the conventional randomized sampling-based algorithm leading to exacerbation of computational complexity and required runtime. The proposed path planning algorithm is named fuzzy greedy rapidly-exploring random tree (FG-RRT). The FG-RRT is equipped with a fuzzy inference system (FIS) consisting of two inputs, one output and nine rules. The first input is a Euclidean function applied in evaluating the quantity of selected parent vertex. The second input is a metaheuristic function applied in evaluating the quality of selected parent vertex. The output indicates the competency of the selected parent vertex for generating a random offspring vertex. This algorithm controls the tree edges growth direction and density in different places of the configuration space concurrently. The proposed method is implemented on a Single Board Computer (SBC) through the xPC Target to evaluate this algorithm. For this purpose four test-cases are designed with different complexity. The results of the Processor-in-the-Loop (PIL) tests indicate that FG-RRT algorithm reduces the required runtime and computational complexity in comparison with the conventional and greedy RRT through fewer number of vertices in planning an initial path in significant manner.

关键词： Holonomic robot processor-in-the-loop test rapidly-exploring random tree sampling-based path planning single board computer

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Control Framework for Trajectory Planning of Soft Manipulator Using Optimized RRT Algorithm

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IEEE ACCESS 2020年 8卷 171730-171743页

作者： Khan, Ameer Tamoor Li, Shuai Kadry, Seifedine Nam, Yunyoung Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Beirut Arab Univ Fac Sci Dept Math & Comp Sci Beirut 11072809 Lebanon Soonchunhyang Univ Dept Comp Sci & Engn Asan 31538 South Korea

This paper proposes a model-free control framework for the path planning of the rigid and soft robotic manipulator using an intelligent algorithm called Weighted Jacobian rapidly-exploring random tree (WJRRT). The optimization approach is used to model the path planning problem, which is independent of the robotic model, and then used the WJRRT algorithm to solve it. WJRRT algorithm not only explores the cartesian space for the end-effector of the robotic manipulator randomly but also directs it towards the goal-position when required. It is robust enough to tackle the uncertainties in the manipulator and make the computation of path planning more efficient. WJRRT assigned a fitness value to each node of the tree. Based on the fitness values algorithm computes the final path, which is a trade-off between efficiency and safety of the path. The simulation results of two, three, and seven degrees of freedom (DOF) robotic manipulators are presented and compared with JT-RRT, Bi-RRT, and TB-RRT algorithms. Experimental results are verified using a soft manipulator made from flexible materials, i.e., polypropylene and polychloroprene. Their flexible structure makes their control complex and creates uncertainties in the model. The simulation and experimental results demonstrate that WJRRT can efficiently and accurately control the motion of manipulators.

关键词： Manipulators Soft robotics Path planning Space exploration Jacobian matrices Robustness rapidly-exploring random tree robotic manipulator redundant manipulator robust path planning soft robotics

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Safe Trajectory Path Planning Algorithm Based on RRT* While Maintaining Moderate Margin From Obstacles

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第11期21卷 3540-3550页

作者： Lim, Subin Jin, Sangrok Pusan Natl Univ Sch Mech Engn 2 Busandaehag Ro63 Beon Gil Busan 46241 South Korea

This paper presents Ex-RRT*, a modification of the rapidly-exploring random tree star (RRT*) algorithm that allows the robot to avoid obstacles with a margin. RRT* generates the shortest path to a destination while avoiding obstacles. However, if the robot's embedded trajectory generation algorithm interpolates the waypoints generated by the RRT* to make a motion, collisions may occur with the edges or overhang of obstacles. This algorithm adds a cost function for the distance from each node to the nearest obstacle to ensure that the waypoints generated by the path planner have an appropriate margin for obstacles. It is designed to provide safer control from collisions when each robot's embedded trajectory generation algorithm operates by interpolating waypoints derived by path planning. Through simulation, we compare the proposed Ex-RRT* and conventional RRT* with performance indices such as total distance traveled and collision avoidance norm. Experiments are conducted on the task of moving an object inside a box with a commercial robot to validate the proposed algorithm. The proposed algorithm generates paths with improved safety and can be applied to various robotic arms and mobile platforms.

关键词： Collision avoidance path planning rapidly-exploring random tree robot manipulation safe path generation

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Real-Time Path Planning Algorithm for Autonomous Border Patrol: Design, Simulation, and Experimentation

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2014年第3-4期75卷 517-539页

作者： Lau, George Liu, Hugh H. T. Univ Toronto Inst Aerosp Studies Toronto ON M3H 5T6 Canada

This paper presents an online path planning algorithm for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle, acting as the pursuer, is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs' Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on rapidly-exploring random trees (RRT) and is capable of producing trajectories within seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

关键词： Real-time path planning Guidance Autonomous rapidly-exploring random tree Pursuit-Evasion Optimal control

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