A novel three-finger dexterous hand with visual-based grasp planning and tactile-based stable grasping

Robotic manipulation is a complex field that still faces numerous challenges. With the development of collaborative robots, the end-effector area is just beginning to gain traction, and electrically driven end-effectors are just starting to be widely used in the industry. One of the fundamental requirements for a robot to achieve practical applications is stable grasping. However, in reality, most stable grasping relies on specific static conditions and human experience. As the robotic industry continues to develop more complex and diverse applications, the need for stable grasping to support these high-level applications increases. To achieve more complex and stable grasping functions, a complex end-effector, such as a dexterous hand, becomes essential hardware.
This thesis focuses on building a novel practical dexterous hand that can be used in robotic manipulation research. To make better use of the dexterous hand, visual-based grasp planning, and tactile-based stable grasping are necessary to form a stable grasping system. In this thesis, three crucial topics were selected and divided into three parts of the work: dexterous hand, grasp planning, and stable grasping.
The dexterous hand part includes the main design work of a modular three-finger dexterous hand called DoraHand and partial work on another one called Eagle Shoal. The performance of the DoraHand and the tactile sensor module is showcased through experiments. Two-finger and five-finger versions of the DoraHand have also been developed and tested in real applications, providing a reliable hardware foundation for further research.
The grasp planning part focuses on providing a grasp planning solution for the dexterous hand. As an essential function of using an end-effector, this part starts with an analytic solution that considers the limitations of the dexterous hand mechanism and grasp quality evaluation. A grasp planning network has been developed using both analytic and data-driven approaches. The network features a multi-finger grasp plan representation method and has been successfully verified.
The stable grasping part is the final application of this thesis, where the hardware provides the foundation and the stable grasping algorithm utilizes the tactile sensor. An open-source visual-tactile dataset has been developed using the Eagle Shoal dexterous hand. The stable grasping algorithm, built based on this dataset, has been successfully verified with different types of end-effectors, including DoraHand and suction cup gripper.
Overall, these three parts of work constitute the critical components of a stable grasping system using a dexterous hand. This system and related dataset enable further research in stable grasping and robotic manipulation.
The primary objective of this thesis has been successfully achieved with the development of DoraHand, which has been used by over twenty research institutes and companies. The algorithms developed for grasp planning and stable grasping serve as a foundation for future research in this field, while the dataset can be used as a benchmark for comprehensive robotic research. Further development is needed to explore the potential applications of the dexterous hand in robotic manipulation.