We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical wholebody controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.

SUMMARYStrawberry is a very delicate fruit that requires special treatment during harvesting. A hierarchical control scheme is proposed based on a fuzzy controller for the force regulation of the gripper and proper grasping criteria, that can detect misplaced strawberries on the gripper or uneven distribution of forces. The design of the gripper and the controller are based on conducted experiments to measure the maximum gripping force and the required detachment force under a variety of detachment techniques. It is demonstrated that the hand motion for detaching the fruit from the stem has a significant role in the process because it can reduce the required force. By analysing those results a robotic gripper with pressure profile sensors is developed that demonstrates an efficiency comparable to the human hand for strawberry grasping. The designed gripper and fuzzy controller performance is tested with a considerable number of fresh fruits to demonstrate the effectiveness to the uncertainties of strawberry grasping.

We present a motion planning and control framework for ALMA, a torque-controlled quadrupedal robot equipped with a six degrees of freedom robotic arm capable of performing dynamic locomotion while executing manipulation tasks. The online motion planning framework, together with a whole-body controller based on a hierarchical optimization algorithm, enable the system to walk, trot and pace while executing tasks such as fixed-position end-effector control, reactive human-robot collaboration and torso posture optimization to increase the arm's kinematic reachability. The torque controllability of the whole system enables the implementation of compliant behavior, allowing a user to safely interact with the robot in a very natural way. We verify our framework on the real robot by performing tasks such as opening a door and carrying a payload together with a human.

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