A Generic Optimization Based Cartesian Controller For Robotic Mobile Manipulation

Typically, the problem of robotic manipulation is divided among two sequential phases: a planning one and an execution one. However, since the second one is executed in open loop, the robot is unable to react in real time to changes in the task (e.g. moving object). This paper addresses the mobile manipulation problem from a real-time, closed loop perspective. In particular, we propose a generic optimization-based Cartesian controller, that given a continuous monitoring of the goal, determines the best motion commands. We target our controller to a robotic system comprising an arm and a mobile platform. However, the approach can in principle be extended to more complex mechanisms. The approach is based on shifting the problem to velocity space, where end effector velocity is a linear function of joint and base platform velocities. Our approach was quantitatively evaluated both on simulation and on a real service robot. It was also integrated into a mobile service robot architecture targeting domestic tasks and evaluated on the RoboCup@Home scientific competition. Our results show that the controller is able to reach random arm configurations with a high probability of success.