There are many ways for a gripper to estimate the forces between its fingers. If powered by direct-drive brushless motors, then one technique is to measure their current. This is not the most accurate technique, but it is simple, keeps the sensor remote, and requires no new components. The estimation involves multiplying current signals through by the torque constant and the inverse transpose of the Jacobian. The Jacobian either amplifies the signal from fingertip force to motor current (at the cost of tip force production), or diminishes it (with the gain of tip force production), indicating an inherent trade-off. However, the Jacobian is a function of configuration, and for any workspace point there are multiple configurations (multiple inverse kinematics solutions), therefore a selection of Jacobian exists. For a given workspace point, the number of Jacobian choices is just a few, but these choices can be designed (through dimensional synthesis) to overcome the trade-off. The problem can be framed as velocity ellipse synthesis over multiple output modes. In this work, we conduct optimal synthesis to compute a new gripper design. The gripper was built and tested. It transitions between two different modes: sense mode and grip mode. Sense mode can sense forces 3 times smaller than grip mode. Grip mode can exert forces 4 times greater than sense mode.

Among primates, the prehensile nature of the hand is vital for greater adaptability and a secure grip over the substrate/branches, particularly for arm-swinging motion or brachiation. Though various brachiation mechanisms that are mechanically equivalent to underactuated pendulum models are reported in the literature, not much attention has been given to the hand design that facilitates both locomotion and within-hand manipulation. In this paper, we propose a new robotic gripper design, equipped with shape conformable active gripping surfaces that can act as an active or passive joint and adapt to substrates with different shapes and sizes. A floating base serial chain, named GraspMaM, equipped with two such grippers, increases the versatility by performing a range of locomotion and manipulation modes without using dedicated systems. The unique gripper design allows the robot to estimate the passive joint state while arm-swinging and exhibits a dual relationship between manipulation and locomotion. We report the design details of the multimodal gripper and how it can be adapted for the brachiation motion assuming it as an articulated suspended pendulum model. Further, the system parameters of the physical prototype are estimated, and experimental results for the brachiation mode are discussed to validate and show the effectiveness of the proposed design.