Abstract This paper presents an efficient multi-fingered grasping force optimization (GFO) method based on generalized penalty-function concepts. In view of the fact that the mainstream multi-fingered GFO method often treats the second-order cone programming (SOCP) problem as a semi-definite programming (SDP) problem, whose computational complexity is high, we hereby use the barrier function to construct the regularized optimization problem. The trade-off representation of different dimension objective functions is given, and the penalty factor is introduced to form the augmented optimization objective function. For specific operational tasks, by adjusting the penalty factor, a more compact, stable or slack, flexible grasping scheme could be obtained. Monte Carlo simulation is used to determine the probability of successful grasping when variability is introduced, and the robustness of the proposed method in the change of contact position and the friction coefficient between hand and object is verified. Experimental results and dynamic simulation are given, which show that the proposed algorithm outperforms the mainstream SDP method in execution time and iteration number, and the obtained force distribution has both continuity and distribution. Operational flexibility is instructive for practical applications.

中文翻译：

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基于广义惩罚函数概念的多指抓取力优化

摘要 本文提出了一种基于广义惩罚函数概念的高效多指抓取力优化（GFO）方法。鉴于主流的多指 GFO 方法往往将二阶锥规划 (SOCP) 问题视为半定规划 (SDP) 问题，其计算复杂度较高，特此采用势垒函数构造正则化优化问题。给出了不同维度目标函数的权衡表示，并引入惩罚因子形成增广优化目标函数。对于特定的操作任务，通过调整惩罚因子，可以获得更紧凑、稳定或松弛、灵活的抓取方案。采用蒙特卡罗模拟确定引入可变性时抓取成功的概率，验证了所提方法在接触位置变化和手与物体摩擦系数变化方面的鲁棒性。给出了实验结果和动态仿真，表明该算法在执行时间和迭代次数上优于主流的SDP方法，得到的力分布具有连续性和分布性。操作灵活性对实际应用具有指导意义。表明该算法在执行时间和迭代次数上优于主流的SDP方法，得到的力分布具有连续性和分布性。操作灵活性对实际应用具有指导意义。表明该算法在执行时间和迭代次数上优于主流的SDP方法，得到的力分布具有连续性和分布性。操作灵活性对实际应用具有指导意义。