Research

Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots (Reuters Press Release) (PhD.pdf)

The research community in legged robotics focuses on bio-inspired robots, although there are some human inventions that nature could not recreate. One of the most significant examples is the wheel that has made our transportation system more efficient and faster, especially in urban environments. Inspired by this human-made evolution, we developed the wheeled-legged robot ANYmal with non-steerable wheels attached to its legs, allowing the robot to be efficient on flat as well as agile on challenging terrain.

This dissertation describes an optimization-based framework to perform complex and dynamic locomotion strategies for robots with legs and wheels. The proposed method allows to perform novel maneuvers, which exploit the wheeled-legged robot's full capabilities over challenging obstacles. By combining innovative techniques in motion control and planning, this work reveals the full potential of wheeled-legged robots and their superiority compared to their legged counterparts. This novel platform, with powered wheels, achieves a speed of 4 m/s on flat terrain, overcomes challenging obstacles with 1.5 m/s, and reduces the cost of transport by 83 % compared to legged systems. The work in this dissertation is published in two conference proceedings and three journal articles.

Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots (IROS2021.pdf) (ICRA2020-Presentation.mp4)

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 approach accurately 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.

Rolling in the Deep – Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization (RA-L2020.pdf)

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 that is fully torque-controlled, including the non-steerable wheels attached to its legs. The robot performs hybrid locomotion with different gait sequences on flat and rough terrain. In addition, we validated the robotic platform at the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, where the robot rapidly maps, navigates, and explores dynamic underground environments.

Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Driving in Challenging Terrain (RA-L2020.pdf)

Wheeled-legged robots are an attractive solution for versatile locomotion in challenging terrain. They combine the speed and efficiency of wheels with the ability of legs to traverse challenging terrain. In this paper, we present a trajectory optimization formulation for wheeled-legged robots that optimizes over the base and wheels' positions and forces and takes into account the terrain information while computing the plans. This enables us to find optimal driving motions over challenging terrain. The robot is modeled as a single rigid-body, which allows us to plan complex motions and still keep a low computational complexity to solve the optimization quickly. The terrain map, together with the use of a stability constraint, allows the optimizer to generate feasible motions that cannot be discovered without taking the terrain information into account. The optimization is formulated as a Nonlinear Programming problem and the reference motions are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The trajectories have been experimentally verified on quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels. Our trajectory optimization framework enables wheeled quadrupedal robots to drive over challenging terrain, e.g., steps, slopes, stairs, while negotiating these obstacles with dynamic motions.

Keep Rollin’ – Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots (RA-L2019.pdf) (Poster.pdf)

We show dynamic locomotion strategies for wheeled quadrupedal robots which combine the advantages of 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 optimizes the generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on the torque-controlled robot ANYmal equipped with non-steerable, torque-controlled wheels. We conducted experiments on flat, inclined and rough terrain, 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.

Trajectory Optimization for Wheeled-Legged Quadrupedal Robots using Linearized ZMP Constraints (RA-L2019.pdf)

We present a trajectory optimizer for quadrupedal robots with actuated wheels. By solving for angular, vertical, and planar components of the base and feet trajectories in a cascaded fashion and by introducing a novel linear formulation of the zero- moment point (ZMP) balance criterion, we rely on quadratic programming only, thereby eliminating the need for nonlinear optimization routines. Yet, even for gaits containing full flight phases, we are able to generate trajectories for executing complex motions that involve simultaneous driving, walking, and turning. We verified our approach in simulations of the quadrupedal robot ANYmal equipped with wheels, where we are able to run the proposed trajectory optimizer at 50 Hz. To the best of our knowledge, this is the first time that such dynamic motions are demonstrated for wheeled-legged quadrupedal robots using an online motion planner.

Skating with a force controlled quadrupedal robot (IROS2018.pdf)

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.

Weaver: Hexapod robot for autonomous navigation on unstructured terrain (RA-L2019.pdf) (JFR2018.pdf) (ICRA2017.pdf) (ISER2016.pdf) (IROS2016.pdf)

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.

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