Keep Rollin’—Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

Abstract

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.

Rolling in the Deep – Hybrid Locomotion for Wheeled-Legged Robots Using Online Trajectory Optimization

“…The online TO of the base motion relies on a ZMP [23]based optimization which continuously updates reference trajectories for the free-floating base. Here, we extend the approach shown in our previous work [11] which originates from the motion planning problem of traditional legged robots [16]. Given the wheel TO in (5), we are now able to generalize the idea of the ZMP to wheeled-legged systems taking into account the moving contact points when in contact.…”

“…For dynamically-consistent motions, our wheel TO takes the rolling constraints of the wheels into account, while our base TO accounts for the robot's balance during locomotion using the idea of the zero-moment point (ZMP) [23]. A hierarchical WBC [11] tracks these motions by computing torque commands for all joints. Our hybrid locomotion framework extends the capabilities of wheeled-legged robots in the following ways: 1) Our framework is versatile over a wide variety of gaits, such as, pure driving, statically stable gaits, dynamically stable gaits, and gaits with full-flight phases.…”

“…For balancing, we add a ZMP inequality constraint, which is described in more detail in the next section, since this is the only part of the base optimization problem which is affected by the computed wheel trajectories in Section III. A complete list of each objective and constraint can be obtained in [11].…”

“…The gravito-inertial wrench [28] is given by f gi = m · (g −r COM ) ∈ R 3 and m gi = m · r COM × (g −r COM ) −l COM ∈ R 3 , where m is the mass of the robot, l COM ∈ R 3 is the angular momentum of the COM, and g ∈ R 3 is the gravity vector. In contrast to [11], [16], the line coefficients d(t) = [p(t) q(t) r(t)] T that describe an edge of a support polygon depend on time t, since the contact points of wheeled-legged robots continue to move even when a leg is in contact, unlike conventional legged robots. The ZMP inequality constraint is sampled over the time horizon t f with a fixed sampling time ∆t = t k − t k−1 .…”

“…As shown in our previous work [11], the computed trajectories in Section III and Section IV are tracked by a hierarchical WBC which generates torque commands for each actuator by accounting for the full rigid body dynamics and physical constraints, i.e., non-holonomic rolling constraint, friction cone, and torque limits. The WBC runs together with state estimation [29] in a 400 Hz loop.…”