Underactuated robotic hands offer high adaptability and control simplicity, yet limited dexterity often constrains their manipulation capabilities. To address this limitation, this article presents the G-raph hand, a reconfigurable anthropomorphic robotic hand designed for stable in-hand manipulation while maintaining control simplicity. Inspired by human manipulation, the design integrates underactuated fingers with a reconfigurable palm featuring a central compliant mechanism. This biomimetic architecture enables both active reconfiguration and passive adaptation by modulating finger-base distribution. Furthermore, a hybrid control scheme of in-hand manipulation is developed, combining position and force-feedback control with a lightweight gait planning strategy based on rapid closure property evaluations. By integrating motion intent and system stability, the proposed framework facilitates stable grasping and significant object reorientation. Kinematic workspace analysis and extensive multifinger manipulation experiments demonstrate that the G-raph hand and its associated control framework achieve reliable, flexible, and anthropomorphic performance in complex tasks.
Qiujie Lu, Chang Liu, Fang Zhang et al.· IEEE Transactions on robotic...· 0 citations
Dimensional parameter optimization of parallel robots faces two efficiency challenges: computationally intensive workspace sampling and the absence of reusable models across changing design requirements. This letter proposes a dual-layer framework. At the evaluation layer, Prior-Radius-Based Boundary Determination (PRBD) and Point-Array Boundary Determination (PABD) provide alternative boundary-focused workspace evaluation methods: PRBD prioritizes speed, while PABD prioritizes boundary accuracy. At the optimization layer, a Multi-Objective Deep Reinforcement Learning (MODRL) algorithm learns reusable policies using discrete actions, attention-enhanced LSTM networks, and a constraint satisfaction reward mechanism. Experiments on a 6-PUS parallel robot show that the proposed boundary-focused methods reduce sampling compared with polar coordinate traversal. C-indicator analysis shows stronger dominance than evolutionary and continuous-action baselines. Constraint satisfaction reward halves convergence time, and discrete actions outperform continuous alternatives. Transferability—the pre-trained policy reduces optimization time by 19.6% for new tasks while achieving a competitive Pareto front. Physical prototypes confirm that solution re-selection addresses design changes without re-optimization.
Fang Zhang, J. Zou, Qiujie Lu et al.· IEEE Robotics and Automation...· 0 citations
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