@InBook{haynes_springer2018,
  pages     = {103--144},
  title     = {Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge},
  publisher = {Springer International Publishing},
  year      = {2018},
  author    = {G. Clark Haynes
               and David Stager
               and Anthony Stentz
               and J. Michael Vande Weghe
               and Brian Zajac
               and Herman Herman
               and Alonzo Kelly
               and Eric Meyhofer
               and Dean Anderson
               and Dane Bennington
               and Jordan Brindza
               and David Butterworth
               and Chris Dellin
               and Michael George
               and Jose Gonzalez-Mora
               and Morgan Jones
               and Prathamesh Kini
               and Michel Laverne
               and Nick Letwin
               and Eric Perko
               and Chris Pinkston
               and David Rice
               and Justin Scheifflee
               and Kyle Strabala
               and Mark Waldbaum
               and Randy Warner},
  editor    = {Matthew Spenko
               and Stephen Buerger
               and Karl Iagnemma},
  volume    = {121},
  series    = {Springer Tracts in Advanced Robotics (STAR)},
  address   = {Cham, Switzerland},
  isbn      = {978-3-319-74666-1},
  abstract  = {CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex
tasks in dangerous, degraded, human-engineered environments, such as those found in disaster response
scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot
performs manipulation tasks using an upright posture, yet it uses more stable prostrate postures for mobility
through difficult terrain. In this paper, we report on the improvements made to CHIMP—both in its mechanical
design and its software systems—in preparation for the DARPA Robotics Challenge Finals in June 2015. These
include details on CHIMP’s novel mechanical design, actuation systems, robust construction, all-terrain mobility,
supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide
an overview of CHIMP’s performance, and we detail the various lessons learned over the course of the challenge.
CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing in 3rd
place out of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a
testament to the robustness engineered into the robot and a remote operator’s ability to execute complex tasks
using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster
response robot.},
  booktitle = {The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue},
  doi       = {10.1007/978-3-319-74666-1_4},
  url       = {https://www.springer.com/de/book/9783319746654},
}

@Article{haynes_jfr2017,
  author   = {G. Clark Haynes
              and David Stager
              and Anthony Stentz
              and J. Michael Vande Weghe
              and Brian Zajac
              and Herman Herman
              and Alonzo Kelly
              and Eric Meyhofer
              and Dean Anderson
              and Dane Bennington
              and Jordan Brindza
              and David Butterworth
              and Chris Dellin
              and Michael George
              and Jose Gonzalez-Mora
              and Morgan Jones
              and Prathamesh Kini
              and Michel Laverne
              and Nick Letwin
              and Eric Perko
              and Chris Pinkston
              and David Rice
              and Justin Scheifflee
              and Kyle Strabala
              and Mark Waldbaum
              and Randy Warner},
  title    = {Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge},
  journal  = {Journal of Field Robotics},
  year     = {2017},
  volume   = {34},
  number   = {2},
  pages    = {281--304},
  abstract = {CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex
tasks in dangerous, degraded, human-engineered environments, such as those found in disaster response
scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot
performs manipulation tasks using an upright posture, yet it uses more stable prostrate postures for mobility
through difficult terrain. In this paper, we report on the improvements made to CHIMP—both in its mechanical
design and its software systems—in preparation for the DARPA Robotics Challenge Finals in June 2015. These
include details on CHIMP’s novel mechanical design, actuation systems, robust construction, all-terrain mobility,
supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide
an overview of CHIMP’s performance, and we detail the various lessons learned over the course of the challenge.
CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing in 3rd
place out of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a
testament to the robustness engineered into the robot and a remote operator’s ability to execute complex tasks
using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster
response robot.},
  doi      = {10.1002/rob.21696},
  eprint   = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21696},
  url      = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21696},
}

@InProceedings{srinivasa_iser2016,
  author    = {Siddhartha S. Srinivasa
               and Aaron M. Johnson
               and Gilwoo Lee
               and Michael C. Koval
               and Shushman Choudhury
               and Jennifer E. King
               and Christopher M. Dellin
               and Matthew Harding
               and David T. Butterworth
               and Prasanna Velagapudi
               and Allison Thackston},
  title     = {A System for Multi-step Mobile Manipulation: Architecture, Algorithms, and Experiments},
  booktitle = {2016 International Symposium on Experimental Robotics},
  year      = {2016},
  editor    = {Dana Kuli{'{c}}
               and Yoshihiko Nakamura
               and Oussama Khatib
               and Gentiane Venture},
  pages     = {254--265},
  address   = {Cham},
  publisher = {Springer International Publishing},
  abstract  = {Household manipulation presents a challenge to robots be-
cause it requires perceiving a variety of objects, planning multi-step mo-
tions, and recovering from failure. This paper presents practical tech-
niques that improve performance in these areas by considering the com-
plete system in the context of this specific domain. We validate these
techniques on a table-clearing task that involves loading objects into a
tray and transporting it. The results show that these techniques improve
success rate and task completion time by incorporating expected real-
world performance into the system design.},
  isbn      = {978-3-319-50115-4},
}

@InProceedings{butterworth_acra2013,
  author    = {David T. Butterworth
               and Boon Siew Han
               and Surya P. N. Singh},
  title     = {Predictably un-predictable — on the implementation of a Walking Pattern Generator for the full-sized humanoid robot HUBO2 using Model Predictive Control},
  booktitle = {Australasian Conference on Robotics and Automation (ACRA)},
  year      = {2013},
  address   = {Sydney, Australia},
  month     = {December},
  abstract  = {There is a large body of research related to WPG
(Walking Pattern Generators) for humanoid robots. Typically
WPG are evaluated based on how well the robot’s actual
ZMP (Zero Moment Point) tracks the desired ZMP trajectory,
using a simulation of a rigid-body robot walking on a solid
floor. However little has been written about how various
approaches scale-up to a full-sized humanoid robot, which has
unmodeled compliance in the joints or contact surfaces, and
makes contact with non-rigid surfaces in the environment like
a soft floor. This paper compares the implementation of three
WPG: Parametrized Polynomials, Preview Control and MPC
(Model Predictive Control), and shows results from simulation
and initial testing on the 1.25m tall humanoid robot HUBO2.},
}

@InProceedings{butterworth_rie2012,
  author    = {David T. Butterworth},
  title     = {Teaching C/C++ programming with Lego Mindstorms},
  booktitle = {3rd International Conference on Robotics in Education (RIE)},
  year      = {2012},
  editor    = {David Obdr{v{z}}{'{a}}lek},
  address   = {Prague, Czech Republic},
  month     = {September},
  abstract  = {Computer programming is a skill required in many
professions, not just computer science. Lego Mindstorms NXT
can be incorporated into a programming course to add hands-on
interactivity that will better engage a broader range of students.
Chosing the most suitable programming language is difficult,
and this paper summarizes some experiences in teaching students
using RoboLab and NXT-G for Mindstorms NXT. The text-based
language RobotC is recommended for beginner and intermediate
level courses, and various code examples are provided to assist
teachers in building lesson plans. It is suggested that advanced
programming should be taught in C++, and an example of using
the NXT++ library to control a robot arm is presented. Teaching
all levels of programming, using robotics, is more enticing and
stimulating for students, and teachers can justify the purchase
of expensive robot hardware by employing it in multiple areas
of the school curriculum.},
}

@MastersThesis{butterworth_thesis2017,
  author   = {David T. Butterworth},
  title    = {A Fast & Efficient Mission Planner for Multi-rotor Aerial Vehicles in Large, High-resolution Maps of Cluttered Environments},
  school   = {Robotics Institute, Carnegie Mellon University},
  year     = {2017},
  address  = {Pittsburgh, PA},
  month    = {May},
  abstract = {Autonomous multi-rotor aerial vehicles have many potential applications
in urban environments, such as inspecting infrastructure, creating 3D maps
from the air, or delivering packages. However this involves flying close to
obstacles like trees and buildings, or flying under overhanging structures like
bridges, powerlines and doorways. Flying safely in environments like this
will require perception and planning with respect to a full 3D world model.

Recent work has demonstrated that multi-rotor UAVs can robustly lo-
calize to static point cloud maps of urban environments using LiDAR- and
VO-based methods. However it is difficult to plan directly in large 3D maps
due to the memory requirements and size of the state space. We investigate
the problem of planning paths in large high-resolution point clouds, for mis-
sions that include flying around obstacles and under overhanging structures.

Our approach is to plan offline a complete global mission path in the static
map, that is collision-free and satisfies altitude constraints. We use a random-
sample based planning algorithm to find approximate shortest paths, with
rejection-sampling to satisfy constraints. We also introduce a novel approach
for avoiding obstacles that may appear on the mission path by pre-planning
a network of alternative paths.

We present results showing a comparison of various path planning al-
gorithms and choose BIT* because it finds the approximate shortest path
in the fastest time. For planning alternative paths, we use geometric path
primitives based on splines or revert to BIT*. We also compare various data
structures for storing the 3D world representation and use a sparse Octree
to store occupied voxels because it offers the best trade-off between storage
memory and collision-checking speed. We present qualitative results showing
the resulting mission paths for multiple environments, including an industrial
site and a tree-filled area.},
  number   = {CMU-RI-TR-17-07},
}

@Bachelorsthesis{butterworth_thesis2013,
  author   = {David T. Butterworth},
  title    = {Implementation of Walking Pattern Generator and Stability Analysis for Biped Robot Walking on Deformable Surface},
  month    = {November},
  year     = {2013},
  abstract = {Previous research has demonstrated that position-controlled biped robots can walk
on rigid surfaces that are flat, lightly sloped or of uneven height. Typically the robot
is assumed to be a perfectly rigid kinematic chain and that foot contact is parallel
to the floor for ZMP walking methods. It is possible to compensate for a small
amount of compliance using feedback control and indeed having some compliance
in the ankle and foot can help to damp out vibration. However, too much ankle
compliance makes the rigid-body biped uncontrollable and too much deformation in
the floor destabilizes the walking gait. This thesis shows that when the robot walks
on a deformable surface the destabilizing effect is comparable to the effect of the
robot being pushed, which both induce a tipping moment around the edge of the
foot. Because the robot is rigid with a floating base, both cases show lateral and/or
fore-aft oscillations about the foot, the difference being that the deformable floor
induces some twist on the landing foot. This leads to the useful conclusion that
existing methods for push recovery on rigid surfaces may be applicable to walking
on a deformable floor, however because parallel foot contact can not be assumed
then additional controllers must be developed to ensure the foot remains in contact
with the non-rigid floor.},
  address  = {Brisbane, Australia},
  school   = {University of Queensland},
}