@misc{patent_WO2020121096_A1,
  author    = {Mauro Aguiar
               and Noam Weinstein
               and Eric Wolff
               and Matthias Sapuan
               and Hsun-Hsien Chang
               and Philipp Robbel
               and Maurilio Di Cicco
               and Guchan Ozbilgin
               and Bishwamoy Roy
               and Yifan Yang
               and Akshay Bhagat
               and David Butterworth
               and Andrew J. Eick
               and Alok Sharma
               and Junqing Wei},
  title     = {Systems and Methods for Sensor Calibration},
  year      = {WIPO Patent WO 2020/121096 A1, June 2020},
  url       = {https://patents.google.com/patent/WO2020121096A1/en},
}

@misc{patent_US11443524_B2,
  author    = {Mauro Aguiar
               and Noam Weinstein
               and Eric Wolff
               and Matthias Sapuan
               and Hsun-Hsien Chang
               and Philipp Robbel
               and Maurilio Di Cicco
               and Guchan Ozbilgin
               and Bishwamoy Sinha Roy
               and Yifan Yang
               and Akshay Bhagat
               and David Butterworth
               and Andrew J. Eick
               and Alok Sharma
               and Junqing Wei},
  title     = {Systems and Methods for Validating Sensor Calibration},
  year      = {US Patent 11,443,524 B2, September 2022},
  url       = {https://patents.google.com/patent/US11443524B2/en},
}

@InBook{haynes_springer2018,
  title     = {Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge},
  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},
  booktitle = {The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue},
  series    = {Springer Tracts in Advanced Robotics (STAR)},
  volume    = {121},
  month     = {April},
  year      = {2018},
  pages     = {103--144},
  editor    = {Matthew Spenko
               and Stephen Buerger
               and Karl Iagnemma},
  publisher = {Springer International Publishing},
  address   = {Cham, Switzerland},
  isbn      = {978-3-319-74666-1},
  doi       = {10.1007/978-3-319-74666-1_4},
  url       = {https://www.springer.com/de/book/9783319746654},

  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.},
}

@Article{haynes_jfr2017,
  title    = {Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge},
  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},
  journal  = {Journal of Field Robotics},
  volume   = {34},
  number   = {2},
  month    = {March},
  year     = {2017},
  pages    = {281--304},
  doi      = {10.1002/rob.21696},
  url      = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21696},

  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.},
}

@InProceedings{srinivasa_iser2016,
  title     = {A System for Multi-step Mobile Manipulation: Architecture, Algorithms, and Experiments},
  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},
  booktitle = {International Symposium on Experimental Robotics},
  address   = {Tokyo, Japan},
  month     = {October},
  year      = {2016},
  pages     = {254--265},
  editor    = {Dana Kuli{'{c}}
               and Yoshihiko Nakamura
               and Oussama Khatib
               and Gentiane Venture},
  publisher = {Springer International Publishing},
  isbn      = {978-3-319-50115-4},

  abstract  = {Household manipulation presents a challenge to robots because it requires perceiving a 
variety of objects, planning multi-step motions, and recovering from failure. This paper presents 
practical techniques that improve performance in these areas by considering the complete 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 realworld performance into the system design.},
}

@InProceedings{butterworth_acra2013,
  title     = {Predictably un-predictable — on the implementation of a Walking Pattern Generator 
for the full-sized humanoid robot HUBO2 using Model Predictive Control},
  author    = {David T. Butterworth
               and Boon Siew Han
               and Surya P. N. Singh},
  booktitle = {Australasian Conference on Robotics and Automation (ACRA)},
  address   = {Sydney, Australia},
  month     = {December},
  year      = {2013},

  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,
  title     = {Teaching C/C++ programming with Lego Mindstorms},
  author    = {David T. Butterworth},
  booktitle = {3rd International Conference on Robotics in Education (RIE)},
  address   = {Prague, Czech Republic},
  month     = {September},
  year      = {2012},
  editor    = {David Obdr{v{z}}{'{a}}lek},

  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,
  title    = {A Fast & Efficient Mission Planner for Multi-rotor Aerial Vehicles in Large, High-resolution Maps of Cluttered Environments},
  author   = {David T. Butterworth},
  month    = {May},
  year     = {2017},
  school   = {Robotics Institute, Carnegie Mellon University},
  address  = {Pittsburgh, United States},
  number   = {CMU-RI-TR-17-07},

  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 localize 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 missions 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 algorithms 
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.},
}

@Bachelorsthesis{butterworth_thesis2013,
  title    = {Implementation of Walking Pattern Generator and Stability Analysis for Biped Robot Walking on Deformable Surface},
  author   = {David T. Butterworth},
  month    = {November},
  year     = {2013},
  school   = {University of Queensland},
  address  = {Brisbane, Australia},

  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.},
}

@InProceedings{zhang_iros2018,
  title     = {P-CAP: Pre-computed Alternative Paths to Enable Aggressive Aerial Maneuvers in Cluttered Environments},
  author    = {Ji Zhang
               and Rushat Gupta Chadha
               and Vivek Velivela
               and Sanjiv Singh},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  address   = {Prague, Czech Republic},
  month     = {October},
  year      = {2018},
  pages     = {8456--8463},

  abstract  = {We propose a novel method to enable fast autonomous flight in cluttered environments. 
Typically, autonomous navigation through a complex environment requires a continuous heuristic search 
on a graph generated by a k-connected grid or a probabilistic scheme. As the vehicle progresses, modification 
of the graph with data from onboard sensors is expensive as is search on the graph, especially if the paths 
must be kino-dynamically feasible. We suggest that computation needed to find safe paths during fast flight 
can be greatly reduced if we precompute and carefully arrange a dense set of alternative paths before the flight. 
Any prior map information can be used to prune the alternative paths to come up with a data structure that enables 
very fast online computation to deal with obstacles that are not on the map but only detected by onboard sensors. 
To test this idea, we have conducted a large number of flight experiments in structured (large industrial facilities) 
and unstructured (forests-like) environments. We show that even in the most unstructured environments, this method 
enables flight at a speed up to 10m/s while avoiding obstacles detected from onboard sensors.},
}

@InProceedings{zhang_fsr2019,
  title     = {P-CAL: Pre-computed Alternative Lanes for Aggressive Aerial Collision Avoidance},
  author    = {Ji Zhang
               and Rushat Gupta Chadha
               and Vivek Velivela
               and Sanjiv Singh},
  booktitle = {12th International Conference on Field and Service Robotics (FSR)},
  address   = {Tokyo, Japan},
  month     = {August},
  year      = {2019},

  abstract  = {We here address the issue of air vehicles flying autonomously at a highspeed in 
complex environments. Typically, autonomous navigation through a complex environment requires a 
continuous heuristic search on a graph generated by a k-connected grid or a probabilistic scheme. 
The process is expensive especially if the paths must be kino-dynamically feasible. Aimed at 
tackling the problem from adifferent angle, we consider the case that the environment is mostly 
known from aprior map. The proposed method suggests the computation needed to find safe paths during 
fast flight can be greatly reduced if we pre-compute and carefully arrange a set of alternative paths 
before the flight. During the navigation, the vehicle selects a pre-computed path to navigate without 
the need to generate a new path. The result is that majority of the processing is migrated to offline 
path generation. Effectively, the onboard computation is significantly reduced, taking < 3% of a CPU 
thread on a modern embedded computer. In experiments, it enables a lightweight aerial vehicle to 
maneuver aggressively through a cluttered forest environment at 10 m/s.},
}