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

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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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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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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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 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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Development of a Mobile Robot for Distribution of Medicine in Hospitals 22

Development of a Mobile Robot for Distribution of Medicine i...

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22nd IFAC Conference on Technology, Culture and International Stability (TECIS)

作者： Stoev, Petko Ficherov, Rosen Georgiev, Momchil Bulgarian Acad Sci Inst Informat & Commun Technol Acad G Bonchev Str 2 Sofia 1113 Bulgaria Tech Univ Sofia 8 Kliment Ohridski blvd Sofia 1000 Bulgaria Bulgarian Acad Sci Space Res & Technol Acad G Bonchev Str 1 Sofia 1113 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. Copyright (C) 2024 The Authors.

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

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Guided Motion Planning for Modular Robots 1

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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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Anytime Dynamic exploring Rapid random tree Approach in Higher Dimension Search Space for Non-Holonomic Robotics

Anytime Dynamic Exploring Rapid Random Tree Approach in High...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Luo, Ren C. Huang, Charly Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781509051021

With the breakthrough of exploring Rapid random tree and several other improvement efforts, the sampling-based motion planning method has been gaining ground in 2D planning and gradually being accepted by many systems as their global planning algorithm. There are many recent approaches on integrating the incremental nature and algorithmic simplicity of sampling-based approach with the agile replanning strategy studied on Incremental Heuristic based motion planning algorithms. Therefore, we develop an anytime dynamic RRT* for non-holonomic systems. We also implement Lyapunov function, which better represents the true-cost-to-go, in order to generate ideal and smoother trajectories. Our method proved to be more robust than some state-of-the-art planning algorithms, with lower cost and smoother path. We evaluate our algorithm with 3 major benchmarks in simulated as well as real-environment. We compared our algorithm with other major planning approaches and proved the cost yields between 8.5%similar to 16.7% less cost and as may reach as low as 58.17% and 95% less than RRT and RRT* respectively.

关键词： Motion Planning optimal path planning sampling-based algorithms rapidly exploring random tree Anytime Dynamic Approach

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Increasing Diversity of Solutions in Sampling-based Path Planning 18

Increasing Diversity of Solutions in Sampling-based Path Pla...

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4th International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Vonasek, Vojtech Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic

ISBN: (纸本)9781450365840

Sampling-based path planning algorithms like rapidly-exploring random trees (RRT) are widely used to solve path planning problems for robots with many Degrees Of Freedom (DOF). Although the sampling-based planners are stochastic and provide different solutions in every trial, many resulting trajectories can be quite similar, e.g., because they are close to each other. Computation of di verse paths, i.e., paths leading through different parts of the configuration space, is important for online navigation of mobile robots or for finding solutions leading through different narrow passages of the configuration space. In this paper, we propose an extension of the RRT algorithm to find diverse paths in the configuration space iteratively. The idea of the proposed method is to prohibit the exploration of selected regions of the configuration space. The prohibited regions are defined using the waypoints of paths computed in the previous iterations. We show that the proposed method finds more diverse trajectories than can be achieved by repeated computations of a single sampling-based planner.

关键词： Path planning rapidly exploring random tree

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Interactive Motion Planning for Steerable Needles in 3D Environments with Obstacles

Interactive Motion Planning for Steerable Needles in 3D Envi...

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3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Patil, Sachin Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781424477098

Bevel-tip steerable needles for minimally invasive medical procedures can be used to reach clinical targets that are behind sensitive or impenetrable areas and are inaccessible to straight, rigid needles. We present a fast algorithm that can compute motion plans for steerable needles to reach targets in complex, 3D environments with obstacles at interactive rates. The fast computation makes this method suitable for online control of the steerable needle based on 3D imaging feedback and allows physicians to interactively edit the planning environment in real-time by adding obstacle definitions as they are discovered or become relevant. We achieve this fast performance by using a rapidly exploring random tree (RRT) combined with a reachability-guided sampling heuristic to alleviate the sensitivity of the RRT planner to the choice of the distance metric. We also relax the constraint of constant-curvature needle trajectories by relying on duty-cycling to realize bounded-curvature needle trajectories. These characteristics enable us to achieve orders of magnitude speed-up compared to previous approaches;we compute steerable needle motion plans in under 1 second for challenging environments containing complex, polyhedral obstacles and narrow passages.

关键词： 3D environments 3D imaging feedback bevel-tip steerable needles complex polyhedral obstacles constant-curvature needle trajectories interactive motion planning medical control systems motion control needles online control position control rapidly exploring random tree reachability-guided sampling heuristics

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