The additional degrees of freedom and missing counterparts in nature make designing locomotion capabilities for wheeled-legged robots more challenging. We propose a whole-body model predictive controller as a single task formulation that simultaneously optimizes wheel and torso motions. Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics. Thanks to the single set of parameters for all behaviors, whole-body optimization makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings . Also, this enables us to reduce the cost of transport of wheeled-legged robots significantly . Our experiments demonstrate highly dynamic motions on a quadrupedal robot with non-steerable wheels in challenging indoor and outdoor environments. Herewith, we verify that a single task formulation is key to reveal the full potential of wheeled-legged robots  .
Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality through the simultaneous optimization over the robot's whole-body variables allows for a single set of parameters and makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings , reducing the cost of transport by up to 85\% . Our experiments demonstrate dynamic motions on a quadrupedal robot with non-steerable wheels in challenging indoor and outdoor environments. The paper's findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot's future state. To this end, we report an improvement of up to 71\% using our proposed single-task approach, making fast locomotion feasible and revealing wheeled-legged robots ' full potential .
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The paper's findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot's future state. To this end, we report an improvement of up to 71\% using our proposed single-task approach, making fast locomotion feasible and revealing
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