Development Of Data Acquisition, Fusion And Transmission System For Robot Grippers

In order to ensure the safety of operation of robots, it is necessary to detect the wrist force of robots. Some robots, however, e.g., an extravehicular mobile robot (EMR) cannot install a wrist force sensor because of the limitation of its condition. A novel kind of technique to estimate the wrist force without additional sensors is presented in this paper, which uses a data fusion method according to the output variations of finger force sensors in a gripper. The calibration experiments are conducted to detect the relationship between the wrist force and finger forces. The experimental data are used to train a radial basis function (RBF) artificial neural network, and the construction and parameters of the network are obtained for data fusion. The results of data fusion of the wrist force are consistent with the practical calibration values, which proves the effectiveness of the wrist force estimating technique proposed in this paper.In order to grasp dexterously objects with the gripper, robots install sensors in their grippers, such as finger force sensors, tactile sensors and a displacement sensor. A DSP based system is developed to implement data acquisition, fusion and transmission of sensors. The hardware of this system consists of a data acquisition module, a DSP module and CAN transmission module. The software includes a monitor program, an initialization module, a watchdog module, a data fusion module, a CAN transmission module, a quadrature encoder pulse count module and an A/D interrupt service program. The system collects and fuses data of sensors in real time, obtains the status of grasping objects with the gripper, and sends this information to a host PC through CAN bus.