Dexterity-Guided Dimensional Synthesis and Multi-Task Control for Fingertip Manipulation

The geometric parameters severely impact the performance of dexterous manipulation, but manually adjusting them is time-consuming. In this paper, we employ the dexterity-guided dimensional synthesis to explore the geometric design space of a robotic hand, and propose a multi-objective optimization framework to improve its manipulation dexterity. Specifically, we first establish a screw-based mathematical model to describe the hand-object system’s kinetostatic properties. Three objectives are specified to achieve Pareto optimality: maximizing the system’s feasible position space, feasible orientation space, and manipulation stability. Furthermore, to validate the optimal design parameters in practice, we develop a multi-task object motion controller and apply it to a letter handwriting task. Finally, the optimized results are quantitatively analyzed by simulation, yielding a Pareto front and 112 optimal solutions. The applicability of the optimal parameters is assessed through a letter “O” handwriting experiment. Results show that the optimized hand can write the “O” with a maximum radius of 30.0 mm, which is 27.12% larger than that of the non-optimized hand. This controller is also used to schedule subtasks with varying priorities to avoid constrained regions. In contrast to the neural network-based PID controller, the designed controller prioritizes the task’s critical parts, ensuring the legibility of the letters. Note to Practitioners—The main motivation of this paper is to propose a multi-objective optimization framework for manipulation dexterity, aimed at enhancing the robotic hand’s ability to manipulate objects and validating its performance through simulation. Currently, most optimization methods primarily focus on a single objective or a single finger, often ignoring the coordinated movements between fingers. This paper begins by establishing a screw-based model to describe the hand-object system’s kinetostatic properties. It then proposes a multi-objective, multi-parameter optimization framework for manipulation dexterity, allowing for a quantitative assessment of how geometric parameters affect object manipulation. Results show that the optimized hand (hand-I) outperforms the non-optimized hand (hand-II) across all three objectives. Another motivation of this paper is to design a multi-task object motion controller to examine the physical applicability of the optimal design parameters through the letter handwriting tasks. Results show that hand-I outperforms hand-II in writing the letter “O”. This controller also addresses the problem of partially constrained object trajectories. The comparative writing experiment of the letter “S” highlights the necessity of this controller and its ability to coordinate multiple subtasks effectively. We hope that the optimization framework for manipulation dexterity, the hand-object system modeling method, and the multi-task object motion controller applied to letter handwriting can provide new insights for other practitioners.