Weight-perception-based novel control for cooperative lifting of objects with a power assist robot by two humans

The objective was to design and implement a weight-perception-based novel control strategy to improve performances when lifting objects with a power assist system by two humans cooperatively. We developed a 1-DOF power assist system for lifting objects. We hypothesized that weight perception due to inertia might be different from that due to gravity when lifting an object with power-assist because the perceived weight is different from the actual weight. The system was simulated and two humans cooperatively lifted objects with it. We critically analyzed weight perception, load forces and motion features. We found that the robot reduced the perceived weights of the cooperatively lifted objects to 25% of the actual weights and the applied load forces were 8 times larger than the actually required load forces. Excessive load forces resulted in excessive accelerations that jeopardized system performances. We then implemented a novel control scheme based on human features that reduced excessive load forces and accelerations and thus enhanced performances in terms of maneuverability, safety etc. The findings may be used to develop power assist robots for manipulating heavy objects in industries that may augment human's abilities and skills and may improve interactions between robots and humans.