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

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 whole-body 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.Comment: IEEE Robotics and Automation Letter

Dynamic locomotion through online nonlinear motion optimization for quadrupedal robots

This paper presents a realtime motion planning and control method which enables a quadrupedal robot to execute dynamic gaits including trot, pace and dynamic lateral walk, as well as gaits with full flight phases such as jumping, pronking and running trot. The proposed method also enables smooth transitions between these gaits. Our approach relies on an online ZMP based motion planner which continuously updates the reference motion trajectory as a function of the contact schedule and the state of the robot. The reference footholds for each leg are obtained by solving a separate optimization problem.The resulting optimized motion plans are tracked by a hierarchical whole-body controller. Our framework has been tested in simulation and on ANYmal, a fully torque-controllable quadrupedal robot, both in simulation and on the actual robot.ISSN:2377-376

Gait and Trajectory Optimization for Legged Systems through Phase-based End-Effector Parameterization

We present a single Trajectory Optimization for- mulation for legged locomotion that automatically determines the gait-sequence, step-timings, footholds, swing-leg motions and 6D body motion over non-flat terrain, without any additional modules. Our phase-based parameterization of feet motion and forces allows to optimize over the discrete gait sequence using only continuous decision variables. The system is represented using a simplified Centroidal dynamics model that is influenced by the feet’s location and forces. We explicitly enforce friction cone constraints, depending on the shape of the terrain. The NLP solver generates highly dynamic motion-plans with full flight-phases for a variety of legged systems with arbitrary morphologies in an efficient manner. We validate the feasibility of the generated plans in simulation and on the real quadruped robot ANYmal. Additionally, the entire solver software TOWR used to generate these motions is made freely available.ISSN:2377-376

Skating with a Force Controlled Quadrupedal Robot

Traditional legged robots are capable of traversing challenging terrain, but lack of energy efficiency when compared to wheeled systems operating on flat environments. The combination of both locomotion domains overcomes the trade-off between mobility and efficiency. Therefore, this paper presents a novel motion planner and controller which together enable a legged robot equipped with skates to perform skating maneuvers. These are achieved by an appropriate combination of planned reaction forces and gliding motions. Our novel motion controller formulates a Virtual Model Controller and an optimal contact force distribution which takes into account the nonholonomic constraints introduced by the skates. This approach has been tested on the torque-controllable robot ANYmal equipped with passive wheels and ice skates as end-effectors. We conducted experiments on flat and inclined terrain, whereby we show that skating motions reduces the cost of transport by up to 80 % with respect to traditional walking gaits

Dynamic Locomotion on Slippery Ground

ISSN:2377-376

Dynamic Locomotion on Slippery Ground

Dynamic locomotion on unstructured and uneven terrain is a challenging task in legged robotics. Especially when it comes to slippery ground conditions, common state estimation and control algorithms suffer from the usual no-slip assumption. In fact, there has been only little research on this subject. This paper addresses the problem of slipping by treating slip detection and recovery tasks separately. Our contribution to the former is a probabilistic slip estimator based on aHidden Markov Model. In the second part of this paper, we propose impedance control and friction modulation as useful tools to recover stability during traction loss. We demonstrate the success of our estimation/control architecture by enabling ANYmal, a quadrupedal torque-controllable robot, to dynamically walk over slippery terrain

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

Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging terrain. In this paper, we present an online trajectory optimization framework for wheeled quadrupedal robots capable of executing hybrid walking-driving locomotion strategies. By breaking down the optimization problem into a wheel and base trajectory planning, locomotion planning for high dimensional wheeled-legged robots becomes more tractable, can be solved in real-time on-board in a model predictive control fashion, and becomes robust against unpredicted disturbances. The reference motions are tracked by a hierarchical whole-body controller that sends torque commands to the robot. Our approach is verified on a quadrupedal robot with non-steerable wheels attached to its legs. The robot performs hybrid locomotion with a great variety of gait sequences on rough terrain. Besides, we validated the robotic platform at the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, where the robot rapidly mapped, navigated and explored dynamic underground environments.ISSN:2377-376