Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators

Abstract

Research interest in redundant mobile manipulators has been constantly increasing during the last decade. The opportunities offered by the redundant degrees of freedom, together with the exploitation of the mobile base, would allow such robots to complete their main task while complying with additional tasks or constraints. These features would make it easier for robots to work in a partly unstructured and dynamic environment, thus increasing production flexibility. In this work, a reactive constraint-based control strategy for mobile manipulators is proposed, which accomplishes a positioning task while simultaneously avoiding unknown and unpredictable obstacles. Differently from other approaches, the trajectory is computed exclusively online, by exploiting the MPC method, without the need of a pre-planned path. Experimental verification on a KUKA youBot shows the applicability of the approach.

Constrained model predictive control for mobile robotic manipulators

“…The approach presented in ref. [19] and inherited from ref. [33] is here developed, extending such constraints on the whole MPC prediction horizon.…”

“…Preliminary results were presented in refs. [19,20]. In particular, in this paper the following advancements are discussed.…”

Predictive Inverse Kinematics for Redundant Manipulators With Task Scaling and Kinematic Constraints

“…In this context, contributions usually address low-level control problems of actuators for trajectory tracking [22]- [24]. Few works also apply predictive techniques to high-level problems such as pointto-point trajectory generation [25] or motion planning for mobile manipulators [26].…”

MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study

“…In [19], a nonlinear model predictive control of a free-flying space robot was developed and the performance of the proposed control was compared with that of a sliding mode control. In [20], a reactive constraint-based control approach was developed for controlling a mobile manipulator so it could reach its goal and avoid unknown and unpredictable obstacles. The robot's trajectory was calculated on-line by using the model predictive method.…”