Admittance neuro-control of a lifting device to reduce human effort

Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system

“…As Figure 1(a) shows, for unimanual lift, the human grasps the object at its center using a power grip of one hand and lifts it with the PARS. However, for bimanual lift, the mechanical design is modified through attaching two lightweight handles to the PAO, and the human grasps the handles using power grips of two hands and lifts the PAO with the PARS, as in Figure 1(b) and (c) (it is different from when the object has only one handle, and the human grasps the handle using one hand and lifts it 8 ). The bimanual lift has two arrangement of force sensors: (i) the human lifts the PAO grasping at two handles, but the PAO is tied to the screw nut through only one force sensor (load cell) at its bottom (we call it ''common force sensor case'' as in Figure 1(b)) and (ii) the PAO is attached to the screw nut directly without any force sensor, the human lifts the PAO grasping at two handles and two separate force sensors (foil strain gauges) are attached to the handles (we call it ''separate force sensors case'' as in Figure 1(c)).…”

“…Most physical human-robot interactions (pHRIs) follow impedance controls, 8 but we derive controls for the PARS differently based on equation (4) as in Figure 2 that may fall within the admittance control (force is input and displacement is output), 8 integrated with velocity control 10 and position feedback. 18 The commanded velocity ( _ x c ) and the position feedback are expressed in equation (5), where _ x c is the input to the servomotor, G is the feedback gain, and the servomotor produces the actuating force according to _ x c .…”

“…A few systems attempted to resolve this problem through gravity compensation. 4,8,15 However, zero gravity removes haptic feelings partly that is not good for haptic manipulation of objects between human and robot. 19 Again, for gravity compensation, the systems need to know the value of object mass.…”

“…20 For handling heavy objects, large inertia, friction, and dynamic effects are expected, which may be compensated and positional accuracy may be provided by admittance controls. 8 Admittance parameters (virtual mass, damping, and stiffness) may affect HRI and manipulation performance. For example, for large admittance parameters, large human force may be required to move the object, human may feel more heaviness (more potential fatigue), movement may be slow (low acceleration), but precise (fine, smooth) manipulation may be achieved.…”

“…7 Dimeas et al proposed admittance neurocontrol of a PARS for lifting objects. 8 Hara and Sankai developed the ''hybrid assistive limb'' to assist human carry objects. 9 In the studies by Gosselin et al 10 and Lecours et al, 11 an assistive robot was proposed for handling large payloads and the safety was provided through the mechanism.…”