Dynamic capture is one of the most challenging issues to be solved in the field of robotics. Although robotic hands/grippers have emerged for decades, there are still few of them that can dynamically capture moving targets because the process is strongly accompanied by impact force and uncertainties in relative gripper/target locations and velocities. In this article, we present the novel design of a multitentacle gripper inspired by the motions of sea anemones. It is found that each tentacle of the sea anemone is not individually able to capture a fish but the collaboration of a larger number of tentacles can greatly enhance the overall capture ability. Based on this concept, 12 continuum arms including active and passive types are proposed and evaluated for their deformation to external forces. In addition, a deployable base, inspired by origami and Sarrus mechanisms, is utilized to change the posture of the continuum arms and drive the active arms. An inertial measurement unit is equipped to sense the impact of the dynamic targets. Finally, a series of experiments proved that the proposed multitentacle gripper could capture dynamic targets with different shapes, velocities, and collision angles, showing satisfactory capture robustness.

In current robot calibration approaches, the error propagation and identification of serial and parallel robots fail to be solved intuitively and generically, resulting in an inefficient calibration implementation and a low accuracy improvement. In this article, we present a generic error modeling method of serial robots and extend to parallel robots by finite and instantaneous screw (FIS) theory. The differential map and the explicit description of FIS on the robot motions enable a concise error modeling of the serial robot. The identifiability of errors in serial robot is discussed. The maximum independent errors are proved to be 4r + 2p + 6, where r and p are the numbers of revolute and prismatic joints, respectively. Based on the error mapping of serial limbs, reciprocal twist and wrench are introduced to consider the interaction among limbs and reveal the error propagation of the parallel robot. Then, the identification algorithms with high robustness and efficiency are investigated for the serial and parallel robots. Specifically, the conventional ill-conditioning problems of parallel robots are addressed. Finally, the proposed kinematic calibration framework for both types of robots are compared with the existing methods, and verified by simulations and experiments. The results show that our calibration approach improves the robot accuracy in a robust and efficient manner.