Design of a Mechanical Gripper with an Integrated Smart Sensor Network for Multi-Axial Force Sensing and Perception of Environment

Abstract :

The dynamic process of grasping different kinds of objects which are pressure sensitive is difficult to handle with classical feedback controllers based on few force sensor values acquired and processed outside of the gripper structure. Side effects like slipping can not be detected at all or too late. Miniaturized smart sensors embedded in structures like grippers can significantly increase the perception of the environment with which a structure interacts. A high-density network of strain-gauge sensors distributed in/on the gripper structure providing local sensor signal-to-information computation can deliver much more suitable informations. Traditionally, strain-gauge sensors are used to measure force in a specific direction. The analog signal acquisition is difficult due to low noise immunity of weak input signals. External signal acquisition with a large distance from sensor to electronics raises noise and reduces signal-to-noise ratio and resolution. We propose and demonstrate the integration of an active smart sensor network into a mechanical gripper structure (finger). The network consists of several highly miniaturized low-power sensor nodes providing sensor signal acquisition, data processing, and communication. Each sensor node can handle up to two strain-gauge sensors detecting different forces at different positions of the gripper structure. The relation between strain and force is derived from FEM simulation of the gripper structure under certain load conditions. Each node performs sensor signal acquisition using a zooming ADC approach, sensor data evaluation, and auto-calibration. Hence, non-calibrated and non-longterm stable sensors can be integrated and used, a prerequisite for robust sensorial materials. It can be demonstrated that an integrated sensor network leads to increasing functionality and robustness. A smart communication protocol is used to provide robust and fault-tolerant communication between nodes and an external interface, for example, a generic processor-based controller. Beside the collection of single force values measured at different positions of the gripper, temporal and spatial composition information derived from the set of measured forces can be computed using data fusion, performed by the nodes of the sensor network itself using distributed computing algorithms. These are overload conditions, force gradients, object recognition and classification, and other higher-level information which can be computed.