The human palm demonstrates spatial reconfigurability during the gripping process and forms a spherical grasping envelope. Based on these observations, this study designs a reconfigurable spherical palm that incorporates a spatial scissor mechanism, which only requires a single actuator to reshape the palm into a range of spherical forms. We conduct a kinematic analysis and modelling of the structure, abstracting three key parameters and analysing their influence on the motion characteristics of the palm. Through multi-objective optimisation, a set of dimensional parameters is derived to balance workspace, human-like motion, and mechanical performance. The performance of the reconfigurability and the grasping capability of the proposed palm is compared to a planar folding palm by superquadrics, and the results show that the spherical design and the reconfigurable characteristics provide larger grasping arrangement and stronger grasping capability of the palm on most of the testing surfaces.

The application of mechanical and other physical properties to the development of robotic systems that can easily adapt to changing external situations is known as mechanical intelligence. Following this concept, many robot hand designs can produce self-adaptive and versatile grasps with simple underactuated fingers and open-loop control, while mechanical- intelligent strategies for dexterous manipulation are still limited. This paper proposes a mechanical-intelligent technique to facilitate dexterous manipulation, in particular prehensile inhand manipulation. The proposed strategy is based on the generation of complex spatial trajectories of the hand-object system, controlled in open loop with the minimum number of actuators and using simple low-level non-position modes. This approach is exemplified by the rigorous analysis and testing of a three-fingered two-actuator underactuated robot hand, called the helical hand, which is capable of generating helical prehensile in-hand manipulation of diversiform objects under error tolerance controlled by constant speed algorithm.

Mechanical intelligence is the use of mechanical and other physical properties to create robotic systems adaptable to new external situations using simple control schemes. Designs of robot hands have successfully been developed and optimised following this principle to produce self-adaptive and versatile power grasps via implementations based on underactuated fingers, elastic components, and open-loop motor control. However, these characteristics, and mechanical-intelligent strategies in general, have been seldom leveraged for precision grasping. This paper proposes a mechanical-intelligent technique to facilitate not only spiral caging power grasp, but also self-adaptive precision grasp with error tolerance. This approach is exemplified by the rigorous analysis, development, and testing of a novel three-fingered, two-actuator, underactuated robot hand, called the helical hand, which is capable of self-adaptive precision grasping, and of generating spiral helical power grasps of unknown objects by simply setting two actuators at a constant speed.

We introduce an underactuated differential-based robot gripper able to perform self-adaptive grasping with passive disturbance rejection. The gripper utilises three car differential systems to achieve self-adaptiveness with a single actuator: a base differential for distributing power from the motor to the fingers, and two independent finger differentials for controlling the proximal and distal joints. Linear and torsional springs are cleverly added to these differentials to allow the return of the fingers and the gripper-object system to equilibrium, thus enabling the gripper rejecting unexpected external forces applied to the fingers after securing a grasp. This novel design provides passive disturbance rejection without implementing complicated control systems and is the main contribution of this paper. Moreover, the differentials allow the gripper to perform not only self-adaptive power grasp but also precision grasp, provide it with a large force transmission efficiency, and facilitate the prediction of grasping position. We analyse the static model of the introduced differential system and evaluate the gripper design via four sets of experiments. Numerical and empirical results clearly demonstrate the viability of the proposed grasper.