Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

Xu, Gaoping; Meng, Zhuo; Li, Shuo; Sun, Yi

doi:10.1017/s026357472200087x

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…2, and the end of the robot is equipped with electric grippers for gripping yarn. As a result, there will be 16 robots working closely together, and multirobot path planning has been proposed in our previous work [3]. The main task of the robot is to store the sorted yarn in the yarn storage mechanism 2 and the yarn winding mechanism 5 to form a yarn opening.…”

Section: The Robots For Preform Weavingmentioning

confidence: 99%

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Path planning for robots in preform weaving based on learning from demonstration

Meng,

Li,

Zhang

et al. 2024

Robotica

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A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

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Section: The Robots For Preform Weavingmentioning

confidence: 99%

“…As a result, robots are gradually being integrated into preform weaving, replacing manual operations of yarn storage. This shift toward automation not only significantly improves efficiency but also reduces labor costs and ensures product consistency [3]. However, path planning is crucial to fully utilize robot technology.…”

Section: Introductionmentioning

confidence: 99%

Path planning for robots in preform weaving based on learning from demonstration

Meng,

Li,

Zhang

et al. 2024

Robotica

Self Cite

View full text Add to dashboard Cite

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“…During training, the goal is to find a policy that maximizes cumulative rewards. However, in practical applications, it is often desirable for a robot's behavior to exhibit a degree of randomness, enabling exploration in unknown environments [29]. One study [17] suggests increasing the randomness of a policy by maximizing its information entropy.…”

Section: Information Entropy In Drlmentioning

confidence: 99%

Optimization of Smart Textiles Robotic Arm Path Planning: A Model-Free Deep Reinforcement Learning Approach with Inverse Kinematics

Zhao,

Ding,

et al. 2024

Processes

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In the era of Industry 4.0, optimizing the trajectory of intelligent textile robotic arms within cluttered configuration spaces for enhanced operational safety and efficiency has emerged as a pivotal area of research. Traditional path-planning methodologies predominantly employ inverse kinematics. However, the inherent non-uniqueness of these solutions often leads to varied motion patterns in identical settings, potentially leading to convergence issues and hazardous collisions. A further complication arises from an overemphasis on the tool center point, which can cause algorithms to settle into suboptimal solutions. To address these intricacies, our study introduces an innovative path-planning optimization strategy utilizing a model-free, deep reinforcement learning framework guided by inverse kinematics experience. We developed a deep reinforcement learning algorithm for path planning, amalgamating environmental enhancement strategies with multi-information entropy-based geometric optimization. This approach specifically targets the challenges outlined. Extensive experimental analyses affirm the enhanced optimality and robustness of our method in robotic arm path planning, especially when integrated with inverse kinematics, outperforming existing algorithms in terms of safety. This advancement notably elevates the operational efficiency and safety of intelligent textile robotic arms, offering a groundbreaking and pragmatic solution for path planning in real-world intelligent knitting applications.

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Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

Cited by 2 publications

References 36 publications

Path planning for robots in preform weaving based on learning from demonstration

Path planning for robots in preform weaving based on learning from demonstration

Optimization of Smart Textiles Robotic Arm Path Planning: A Model-Free Deep Reinforcement Learning Approach with Inverse Kinematics

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