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---
layout: default
title: Research
permalink: /research/
---
<h1>
Research
</h1>
<small> Dissertation </small>
<h4>
<i>Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots</i>
<a href=https://youtu.be/b_ey3hXAcfU>(Reuters Press Release)</a>
<a href=https://www.research-collection.ethz.ch/handle/20.500.11850/515694>(PhD.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/39rRhTqcQc0"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<p style="text-align:justify">
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.
</p>
<small> March, 2021  ·  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) </small>
<h4>
<i>Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots</i>
<a href=/publications/files/2021_iros_bjelonic.pdf>(IROS2021.pdf)</a>
<a href=https://youtu.be/tf_twcbF4P4>(ICRA2020-Presentation.mp4)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/_rPvKlvyw2w"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> March, 2020  ·  IEEE Robotic and Automation Letters (RA-L) </small>
<h4>
<i>Rolling in the Deep – Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization</i>
<a href=/publications/files/2020_ral_bjelonic.pdf>(RA-L2020.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/ukY0vyM-yfY"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<div class="container">
<iframe src="https://www.youtube.com/embed/tf_twcbF4P4"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> April, 2020  ·  IEEE Robotic and Automation Letters (RA-L) </small>
<h4>
<i>Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Driving in Challenging Terrain</i>
<a href=/publications/files/2020_ral_medeiros.pdf>(RA-L2020.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/DlJGFhGS3HM"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> January, 2019  ·  IEEE Robotic and Automation Letters (RA-L) </small>
<h4>
<i>Keep Rollin’ – Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots</i>
<a href=/publications/files/2019_ral_bjelonic.pdf>(RA-L2019.pdf)</a>
<a href=/publications/files/2019_ral_bjelonic_poster.pdf>(Poster.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/nGLUsyx9Vvc"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> January, 2019  ·  IEEE Robotic and Automation Letters (RA-L) </small>
<h4>
<i>Trajectory Optimization for Wheeled-Legged Quadrupedal Robots using Linearized ZMP Constraints</i>
<a href=/publications/files/2019_ral_de_viragh.pdf>(RA-L2019.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/I1aTCTc0J4U"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> March, 2018  ·  IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS) </small>
<h4>
<i>Skating with a force controlled quadrupedal robot</i>
<a href=/publications/files/2018_iros_bjelonic.pdf>(IROS2018.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/fJfAWiylpxw"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>
<small> November, 2015 - September, 2016  ·  Master thesis </small>
<h4>
<i>Weaver: Hexapod robot for autonomous navigation on unstructured terrain</i>
<a href=/publications/files/2019_ral_buchanan.pdf>(RA-L2019.pdf)</a>
<a href=/publications/files/2018_jfr_bjelonic.pdf>(JFR2018.pdf)</a>
<a href=/publications/files/2017_icra_bjelonic.pdf>(ICRA2017.pdf)</a>
<a href=/publications/files/2016_iser_homberger.pdf>(ISER2016.pdf)</a>
<a href=/publications/files/2016_iros_bjelonic.pdf>(IROS2016.pdf)</a>
</h4>
<div class="container">
<iframe src="https://www.youtube.com/embed/eLMUiX96En0"
frameborder="0" allowfullscreen class="video"></iframe>
</div>
<div style="padding-top:10px"></div>
<p style="text-align:justify">
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.
</p>
<div style="padding-top:30px"></div>