People with severe mobility impairment such as quadriplegia require help from human assistants to manage activities of daily living. Various assistive robotic devices have been proposed and some are commercially available, but they mostly have limited functionalities. We propose a cost-effective mobile robotic ma- nipulator, BOW, or Baxter-on-Wheels, suitable for operations by mobility impaired but cognitively sound individuals. The BOW combines a human-friendly industrial robot (Baxter by Rethink Robotics) with a commercial electric wheelchair for an integrated and versatile, yet low cost, system. The human user can typically only command a small number of degrees of freedom due to the limitation of motion range or strength. To determine the complete robot motion, we propose a shared control strategy blending the human command with autonomous redundancy reso- lution. The resolved velocity algorithm solves an on-line optimization matching the robot motion with the human commanded motion. Additional considerations such as collision prevention, singularity avoidance, satisfaction of joint limits, and ex- clusion of non-intuitive base motion, are incorporated as part of the optimization objective function or constraints. This constrained optimization problem is strictly convex and may be efficiently solved as a quadratic program. This approach allows multiple modes of operations, selectable by the user, including: end-effector posi- tion control, end-effector orientation control, combined position/orientation control, force control, and dual-arm control. We present the experimental results of two il- lustrative applications on the BOW: end-effector position control for a pick-and- place task and a board cleaning task involving both motion and force control. In both cases, the user only provides a 3-degree-of-freedom command, but can still effectively manipulate the motion and force of the robot end-effector, while the au- tonomous controller provides intuitive and safe internal motion.
in Trends in Control and Decision-Making for Human–Robot Collaboration Systems, Ed. by Y. Wang and F. Zhang, Springer-Verlag, London, U.K., 2017.