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Article
Publication date: 16 August 2024

Yahao Wang, Yanghong Li, Zhen Li, HaiYang He, Sheng Chen and Erbao Dong

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling…

65

Abstract

Purpose

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

Design/methodology/approach

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Findings

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

Originality/value

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

Details

Robotic Intelligence and Automation, vol. 44 no. 5
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 22 March 2024

Yahao Wang, Zhen Li, Yanghong Li and Erbao Dong

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new…

155

Abstract

Purpose

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

Design/methodology/approach

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Findings

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

Originality/value

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 3
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 28 January 2025

Yanghong Li, Yahao Wang, Zhen Li, Lv Yingxiang, Jin Chai and Erbao Dong

This paper aims to design a deep reinforcement learning (DRL)-based variable impedance control policy that supports stability analysis for robot force tracking in complex…

26

Abstract

Purpose

This paper aims to design a deep reinforcement learning (DRL)-based variable impedance control policy that supports stability analysis for robot force tracking in complex geometric environments.

Design/methodology/approach

The DRL-based variable impedance controller explores and pre-learns the optimal policy for impedance parameter tuning in simulation scenarios with randomly generated workpieces. The trained results are then used as feedforward inputs to improve the force-tracking performance of the robot during contact. Based on Lyapunov’s theory, the stability of the proposed control policy is analysed to illustrate the interpretability of the results.

Findings

Simulations and experiments are performed on different types of complex environments. The results show that the proposed method is not only theoretically feasible but also has better force-tracking effects in practice.

Originality/value

Compared with most other DRL-based control policies, the proposed method possesses stability and interpretability, effectively avoids the overfitting phenomenon and thus has better simulation-to-real deployment results.

Details

Robotic Intelligence and Automation, vol. 45 no. 1
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 13 February 2024

Yanghong Li, Yahao Wang, Yutao Chen, X.W. Rong, Yuliang Zhao, Shaolei Wu and Erbao Dong

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high…

204

Abstract

Purpose

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

Design/methodology/approach

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Findings

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

Practical implications

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

Originality/value

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 8 January 2018

Chi-Cheng Huang and Ping-Kuo Chen

This study aims to explore the influence of social interaction processes on transactive memory system (TMS) practice, the mediation of knowledge integration to the relationship…

1326

Abstract

Purpose

This study aims to explore the influence of social interaction processes on transactive memory system (TMS) practice, the mediation of knowledge integration to the relationship between TMS and team performance and the moderation of team psychological safety to the relationship among TMS, knowledge intentions and team performance.

Design/methodology/approach

The authors collected data from a sample of 366 team members from 55 research and development (R&D) teams in Taiwan and conduct the analysis using the partial least squares method.

Findings

The results of this study indicate that social interaction processes have a positive effect on a TMS; a TMS can foster team performance, but knowledge integration mediates the relationship between the TMS and team performance; and team psychological safety can moderate the relationship between the TMS, knowledge integration and team performance.

Originality/value

Existing studies not only fail to explore the influence of social interaction processes on a TMS practice but also lack empirical analyses to explore knowledge integration as a mediator and team psychological safety as a moderator. This study fills that gap by developing a model that includes these types of relationships and suggests the importance of the TMS in the context of R&D.

Details

Journal of Knowledge Management, vol. 22 no. 1
Type: Research Article
ISSN: 1367-3270

Keywords

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