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Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots

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SENSORS 2024年第21期24卷 6948页

作者： Liu, Sisi Zhao, Zhan Wei, Jun Zhou, Qianqian Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Peoples R China

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

关键词： rapidly exploring random tree kinematic constraints wheeled mobile robots post waypoint shift shortest path planning

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Improved path planning algorithm for mobile robots

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SOFT COMPUTING 2023年第20期27卷 15057-15073页

作者： Sun, Liping Duan, Xiaoyu Zhang, Kai Xu, Pingan Zheng, Xiaoyao Yu, Qingying Luo, Yonglong Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Anhui Peoples R China Anhui Normal Univ Anhui Prov Key Lab Network & Informat Secur Wuhu 241002 Anhui Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China

Path planning is an NP-hard problem in road network environments. Considering that the existing path planning algorithms mainly have the problems of low smoothness and low search efficiency in generating paths in large-scale complex environments, an improved rapidly exploring random tree (RRT) algorithm is proposed in this paper. First, the grid method is applied to model the road network environment, and the RRT algorithm based on adjacency expansion is proposed to search the initial path. Then, the strategies of identifying paths and eliminating redundant paths are adopted, respectively, to further optimize the selected paths. Experimental results show that, compared with other path planning algorithms, our algorithm can achieve faster convergence speed, shorter search path, and better smoothness in a complex map of the environment.

关键词： Path planning rapidly exploring random tree Adjacent-relation extension Repetitive sequence optimization

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Degeneracy-Aware Full-Pose Path Planning Strategy for Robot Manipulator

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第8期54卷 4955-4965页

作者： Shen, Henghua Xie, Wen-Fang Zhu, Ningyu Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada

In this article, we present a novel full-pose path planning strategy with degenerate direction avoidance for robot manipulators. Effort seeks to define an efficient multiobjective cost function for finding an optimal path in task space by minimizing the path length, and, meanwhile, maximizing orthogonality to the degenerate direction of the path. Since a robot manipulator by nature is a noncommensurate system, the unit dual quaternion is used to unify the pose representation and formulate the cost function of path length. Additionally, to justify the robot's ability to maneuver, another cost evaluation of orthogonality is developed on the basis of the analytical Jacobian matrix decomposition. The optimized rapidly exploring random tree (RRT*) is, then, applied to execute the synthesized cost function in planning a path for a six-degree-of-freedom (6-DOF) FANUC-M-20iA industrial robot. By comparing with two relevant path planning methods, simulation analysis results illustrate the superior performance of the proposed path planning approach in terms of path direction and joint change.

关键词： Degeneracy path planning rapidly exploring random tree robot manipulator unit dual quaternion

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Online motion planning of mobile cable-driven parallel robots for autonomous navigation in uncertain environments

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 397-412页

作者： Xu, Jiajun Kim, Byeong-Geon Feng, Xiguang Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea

Mobile cable-driven parallel robots (MCDPRs) offer expanded motion capabilities and workspace compared to traditional cable-driven parallel robots (CDPRs) by incorporating mobile bases. However, additional mobile bases introduce more degree-of-freedom (DoF) and various constraints to make their motion planning a challenging problem. Despite several motion planning methods for MCDPRs being developed in the literature, they are only applicable to known environments, and autonomous navigation in unknown environments with obstacles remains a challenging issue. The ability to navigate autonomously is essential for MCDPRs, as it opens up possibilities for the robot to perform a broad range of tasks in real-world scenarios. To address this limitation, this study proposes an online motion planning method for MCDPRs based on the pipeline of rapidly exploring random tree (RRT). The presented approach explores unknown environments efficiently to produce high-quality collision-free trajectories for MCDPRs. To ensure the optimal execution of the planned trajectories, the study introduces two indicators specifically designed for the mobile bases and the end-effector. These indicators take into account various performance metrics, including trajectory quality and kinematic performance, enabling the determination of the final following trajectory that best aligns with the desired objectives of the robot. Moreover, to effectively handle unknown environments, a vision-based system utilizing an RGB-D camera is developed, allowing for precise MCDPR localization and obstacle detection, ultimately enhancing the autonomy and adaptability of the MCDPR. Finally, the extensive simulations conducted using dynamic simulation software (CoppeliaSim) and the on-board real-world experiments with a self-built MCDPR prototype demonstrate the practical applicability and effectiveness of the proposed method.

关键词： Mobile cable-driven parallel robot Autonomous navigation rapidly exploring random tree Stability and kinematic performance Vision system

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A Global Path Planning Algorithm for Mobile Robot in Cluttered Environments with an Improved Initial Cost Solution and Convergence Rate

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第3期47卷 3633-3647页

作者： Ganesan, Sivasankar Natarajan, Senthil Kumar Srinivasan, Jeevaanand Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi 626005 Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

Sampling-based path planning algorithms are popularly used in autonomous mobile robot navigation applications. Optimal rapidly exploring random trees (RRT*) is one of the well-known sampling-based single-query path planning algorithms and it is asymptotically optimal, but its convergence is slow. To address the slow convergence problem of the RRT* algorithm, this paper proposes a directional RRT* algorithm called D-RRT*. The key idea of D-RRT* is to reduce the sampling space. This is achieved in this proposed work by focusing on the direction of the goal from the starting configuration through a simple elliptical heuristic formed between them. The proposed methodology is validated in two different cluttered 2D environments and compared with existing algorithms. The proposed D-RRT* path planning algorithm outperforms the RRT* in three performance measures: the initial cost solution, convergence time, and the number of nodes visited. The convergence rate of the proposed D-RRT* is improved over RRT* by 8.5% and 14.7% in the two cluttered environments considered. Also, the proposed D-RRT* algorithm is validated in a real-time environment using the TurtleBot3 robot.

关键词： rapidly exploring random tree Directional Sampling-based path planning algorithms Mobile robots

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Optimal sampling-based path planning for mobile cable-driven parallel robots in highly constrained environment

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COMPLEX & INTELLIGENT SYSTEMS 2023年第6期9卷 6985-6998页

作者： Xu, Jiajun Kim, Byeong-Geon Lu, Yuzhen Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea Michigan State Univ Dept Biosyst & Agr Engn E Lansing MI 48824 USA

Mobile cable-driven parallel robots (MCDPRs) is a novel concept of cable-driven parallel robots (CDPRs) developed by mounting several mobile bases to discrete the conventional fixed frame. However, the additional mobile bases introduce more degree-of-freedom (DoF), thereby causing the kinematic redundancy. Moreover, mobile bases are susceptible to disturbances causing inconsistent performance. Hence, path planning of MCDPRs becomes a challenging issue due to various internal and external constraints involved. In this article, an optimization-based path planning method is proposed for MCDPRs in highly constrained environments with considering kinematic stability. The proposed approach quickly generates feasible paths for coupled mobile bases by using the developed multi-agent rapidly exploring random tree (MA-RRT). In this process, the tree can share information through the heuristics method to optimize the path, and the adaptive sampling strategy is thus proposed to increase the tree growth efficiency by self-adjusting sampling space. Moreover, the developed dynamic control checking method (DCC) is integrated with MA-RRT to smooth the path and the kinodynamic constraints of mobile bases can be satisfied. To generate the path of the end-effector, two performance metrics are designed considering the kinematic and stability of the MCDPR. Based on the performance metrics, the grid-based search method is developed to generate the path for the end-effector. Finally, the convincing performance of the proposed method is revealed through the dynamic simulation software (CoppeliaSim) and real-world experiments based on a self-built MCDPR prototype.

关键词： Mobile cable-driven parallel robot rapidly exploring random tree Stability and kinematic performance Optimal sampling-based path planning

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Data-driven child behavior prediction system based on posture database for fall accident prevention in a daily living space

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第12期11卷 5845-5855页

作者： Nose, Tsubasa Kitamura, Koji Ono, Mikiko Nishida, Yoshifumi Ohkura, Michiko Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Koto Ku 2-4-7 Aomi Tokyo 1350064 Japan Shibaura Inst Technol Koto Ku 3-7-5 Toyosu Tokyo 1358548 Japan Tokyo Inst Technol Meguro Ku 2-12-1 Ookayama Tokyo 1528552 Japan Chuo Univ Bunkyo Ku 1-13-27 Kasuga Tokyo 1128551 Japan

Ten thousand children are admitted to emergency rooms due to accidents every year in Tokyo. The most frequent accident is a fall accident. Fall accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. Predicting children's behavior is necessary in order to improve the environment. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating cameras, a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. Thirty five children participated in an experiment to collect climbing posture data. A simulation is performed based on the posture database and allows us to visually understand how children climb up in daily living space. This makes it possible to improve to achieve a safe environment for children without the need for specialized knowledge, which is useful for parents, nursery teachers, nurses, etc. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.

关键词： Smart home Injury prevention Climbing behavior Data-driven simulation Configuration space rapidly exploring random tree

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A collaborative path planning method for mobile cable-driven parallel robots in a constrained environment with considering kinematic stability

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 4857-4868页

作者： Xu, Jiajun Kim, Byeong-Geon Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea

Mobile cable-driven parallel robot (MCDPR) is a variant of cable-driven parallel robots (CDPRs) by mounting several mobile bases to replace the conventional fixed frame. The novel modification of adding mobile bases leads MCDPRs being highly flexible and have great potential for complex environments. However, the issue of coupled mobile bases introduces actuated kinematic redundancies which present challenges for path planning. In this paper, we propose a collaborative path planning method for MCDPRs, and it allows the robot to deal with complex internal and external constraints in a high-dimensional state space efficiently. The proposed method quickly generates feasible paths for coupled mobile bases using the adaptive goal-biased rapidly exploring random tree (RRT) method, in which the adaptive sampling method is developed to enhance efficiency. Based on the feasible path of the mobile base, we proposed a grid-based search method to determine the position of the end-effector with considering the stability and kinematic performances. Furthermore, the planned paths are post-processed with the cubic splines to obtain continuous profiles for the robot. Finally, the proposed method is validated through the dynamic simulation software (CoppeliaSim) and experiments based on a MCDPR prototype with an eight-cable-driven parallel robot mounted on four mobile bases.

关键词： Mobile cable-driven parallel robot rapidly exploring random tree Stability and kinematic performance Path planning

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Narrow Passage Problem Solution for Motion Planning 20th

Narrow Passage Problem Solution for Motion Planning

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20th Annual International Conference on Computational Science (ICCS)

作者： Szkandera, Jakub Kolingerova, Ivana Manak, Martin Univ West Bohemia Fac Appl Sci Dept Comp Sci & Engn Univ 8 Plzen 30614 Czech Republic New Technol Informat Soc Univ 8 Plzen 30614 Czech Republic

ISBN: (纸本)9783030503710;9783030503703

The paper introduces a new randomized sampling-based method of motion planning suitable for the problem of narrow passages. The proposed method was inspired by the method of exit points for cavities in protein models and is based on the rapidly exploring random tree (RRT). Unlike other methods, it can also provide locations of the exact positions of narrow passages. This information is extremely important as it helps to solve this part of space in more detail and even to decide whether a path through this bottleneck exists or not. For data with narrow passages, the proposed method finds more paths in a shorter time, for data without narrow passages, the proposed method is slower but still provides correct paths.

关键词： Motion planning Sample based algorithms rapidly exploring random tree Narrow passage Bottleneck

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Guided Motion Planning for Modular Robots 1

Guided Motion Planning for Modular Robots

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1st International Workshop on Modelling and Simulation for Autonomous Systems (MESAS)

作者： Vonasek, Vojtech Penc, Ondrej Preucil, Libor Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Fac Nucl Sci & Phys Eng Brehova 7 Prague 11519 Czech Republic

ISBN: (纸本)9783319138237;9783319138220

Modular robots consist of many modules that can be connected into various structures. This allows modular robots to adapt their shape according to a given task and environment. To visit a desired place in an environment, motion planning is required. To generate feasible plans for these robots with many degrees of freedom and many kinematic and dynamic constraints, a physical simulation is required to precisely model motion of the robots. As the physical simulation can be time consuming, motion planners need to be extended to decrease the number of simulation queries. In this paper, we propose a novel motion planning for modular robots based on guided sampling of configuration space. The guided sampling utilizes a precomputed simple path in the environment, along which the configuration space of the robot is sampled with higher probability. This approach significantly decreases the number of calls of physical simulations which increases the speed of the planning process.

关键词： motion planning physical simulation modular robots rapidly exploring random tree

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