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Mobile manipulators, which are intrinsically redundant when the manipulator and mobile base are moving together, are known for their capabilities to carry out multiple tasks at the same time. This paper presents a whole-body control framework, inspired by legged bio-robots, for a velocity controlled non-holonomic mobile manipulator based on task priority. Control primitives, such as manipulability optimization, trajectory tracking of the end-effector and mobile base, and collision avoidance, are considered in the framework and arranged at different priorities. Lower priority tasks are projected into the null space of control tasks with higher priorities. As a result, lower level tasks are completed without affecting the performance of higher priority tasks. Several experiments are implemented to verify the effectiveness of the proposed controller. The proposed method is proved to be an effective way to solve the whole-body control problem of velocity controlled mobile manipulators.

Purpose This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator. Design/methodology/approach The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations. Findings To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators. Originality/value The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

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