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ros_control: A generic and simple control framework for ROS

December 2017
The Journal of Open Source Software 2(20):456

December 2017
2(20):456

DOI:10.21105/joss.00456

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CC BY 4.0

Authors:

Sachin Chitta

SRI International

Vijay Pradeep

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ros_control: A generic and simple control framework for

ROS

Sachin Chitta9, 11, Eitan Marder-Eppstein1, Wim Meeussen1, Vijay

Pradeep1, Adolfo Rodríguez Tsouroukdissian12, 2, Jonathan Bohren8, 10,

David Coleman3, 11, Bence Magyar4, 2, Gennaro Raiola5, 2, Mathias

Lüdtke6, and Enrique Fernandez Perdomo7, 2

1hiDOF, Inc. (at the time of this work) 2PAL Robotics (at the time of this work) 3PickNik

Consulting 4Heriot-Watt University, Edinburgh Centre for Robotics 5Department of Advanced

Robotics, Istituto Italiano di Tecnologia (IIT) 6Fraunhofer IPA 7Clearpath Robotics 8Honeybee

Robotics 9Kinema Systems Inc. 10 John Hopkins University 11 Willow Garage Inc. (at the time of

this work) 12 Pick-it NV

DOI: 10.21105/joss.00456

Software

•Review

•Repository

•Archive

Submitted: 09 November 2017

Published: 05 December 2017

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Authors of JOSS papers retain

der a Creative Commons Attri-

bution 4.0 International License

(CC-BY).

Summary

In recent years the Robot Operating System (Quigley et al. 2009) (ROS) has become

the ‘de facto’ standard framework for robotics software development. The ros_control

framework provides the capability to implement and manage robot controllers with a

focus on both real-time performance and sharing of controllers in a robot-agnostic way.

The primary motivation for a sepate robot-control framework is the lack of realtime-

safe communication layer in ROS. Furthermore, the framework implements solutions for

controller-lifecycle and hardware resource management as well as abstractions on hard-

ware interfaces with minimal assumptions on hardware or operating system. The clear,

modular design of ros_control makes it ideal for both research and industrial use and

has indeed seen many such applications to date. The idea of ros_control originates from

the pr2_controller_manager framework specic to the PR2 robot but ros_control is

fully robot-agnostic. Controllers expose standard ROS interfaces for out-of-the box 3rd

party solutions to robotics problems like manipulation path planning (MoveIt! (Chitta,

Sucan, and Cousins 2012)) and autonomous navigation (the ROS navigation stack).

Hence, a robot made up of a mobile base and an arm that support ros_control doesn’t

need any additional code to be written, only a few controller conguration les and it is

ready to navigate autonomously and do path planning for the arm. ros_control also

provides several libraries to support writing custom controllers.

Figure 1: Overview

Chitta et al., (2017). ros_control: A generic and simple control framework for ROS. Journal of Open Source Software, 2(20), 456,

doi:10.21105/joss.00456

Figure 2: ROS Control overview

Packages and functionalities

The backbone of the framework is the Hardware Abstraction Layer, which serves as

a bridge to dierent simulated and real robots. This abstraction is provided by the

hardware_interface::RobotHW class; specic robot implementations have to inherit

from this class. Instances of this class model hardware resources provided by the robot

such as electric and hydraulic actuators and low-level sensors such as encoders and

force/torque sensors. It also allows for integrating heterogeneous hardware or swapping

out components transparently whether it is a real or simulated robot.

There is a possibility for composing already implemented RobotHW instances which is

ideal for constructing control systems for robots where parts come from dierent suppliers,

each supplying their own specic RobotHW instance. The rest of the hardware_interface

package denes read-only or read-write typed joint and actuator interfaces for abstracting

hardware away, e.g. state, position, velocity and eort interfaces. Through these typed

interfaces this abstraction enables easy introspection, increased maintainability and con-

trollers to be hardware-agnostic.

The controller_manager is responsible for managing the lifecycle of controllers, and

hardware resources through the interfaces and handling resource conicts between con-

trollers. The lifecycle of controllers is not static. It can be queried and modied at

runtime through standard ROS services provided by the controller_manager. Such

services allow to start, stop and congure controllers at runtime.

Furthermore, ros_control ships software libraries addressing real-time ROS communi-

cation, transmissions and joint limits. The realtime_tools library adds utility classes

handling ROS communications in a realtime-safe way. The transmission_interface

Chitta et al., (2017). ros_control: A generic and simple control framework for ROS. Journal of Open Source Software, 2(20), 456,

doi:10.21105/joss.00456

package supplies classes implementing joint- and actuator-space conversions such as: sim-

ple reducer, four-bar linkage and dierential transmissions. A declarative denition of

transmissions is supported directly with the kinematics and dynamics description in the

robot’s Universal Robot Description Format (URDF) (Willow Garage 2009) le. The

joint_limits_interface package contains data structures for representing joint limits,

methods to populate them through URDF or yaml les and methods to enforce these lim-

its. control_toolbox oers components useful when writing controllers: a PID controller

class, smoothers, sine-wave and noise generators.

The repository ros_controllers holds several ready-made controllers supporting

the most common use-cases for manipulators, mobile and humanoid robots, e.g. the

joint_trajectory_controller is heavily used with position-controlled robots to

interface with MoveIt!. Finally, control_msgs provides ROS messages used in most

controllers oered in ros_controllers.

ros_control was conceptualized by Sachin Chitta at Willow Garage Inc. and initial

design and implementation was done by Sachin Chitta (then at Willow Garage), Wim

Meussen, Vijay Pradeep and Eitan Marder-Epstein (then at HiDOF) before being released

open-source.

ros_control is released as binary packages with each new version of ROS, source code is

hosted at the ros-controls Github organization. Documentation on behaviour, interfaces,

doxygen-generated pages and tutorials can be found at ros_control and ros_controllers.

For a thorough presentation we invite the interested reader to watch the talk given at

ROSCon2014 (Adolfo Rodríguez Tsouroukdissian, n.d.).

Robots using ros_control

Being a mature framework, ros_control is widely applied to both production and re-

search platform robots. A few examples where the control system is implemented with

ros_control are:

• Clearpath Robotics’ outdoor mobile robots: Grizzly, Husky, Jackal (“New Universal

Robots Driver Makes Manipulation Research Easier,” n.d.), and OTTO Motors’

industrial indoor mobile robots: OTTO 1500, OTTO 100

• The “Twil” robot at Federal University of Rio Grande do Sul (Lages 2017)

• The quadruped robots HyQ and HyQ2Max (Semini et al. 2011, Semini et al. (2017))

at Istituto Italiano di Tecnologia

• NASA’s humanoid and biped robots: Valkyrie & Robonaut (N. A. Radford et al.

2015, Hart et al. (2014), Badger et al. (2016))

• PAL Robotics’ humanoid, biped and mobile robots: REEM, REEM-C, PMB2,

Tiago and Talos (Stasse et al. 2017)

• Shadow Robot’s anthropomorphic, highly sensorized and precise Shadow Hand

(Meier et al. 2016)

• Universal Robots’ industrial arms: UR3, UR5 (Andersen 2015)

Chitta et al., (2017). ros_control: A generic and simple control framework for ROS. Journal of Open Source Software, 2(20), 456,

doi:10.21105/joss.00456

Robot names and credits in order of appearance: Valkyrie (Photo by NASA / Bill

Staord), Husky (Photo by Clearpath Robotics), OTTO (Photo by Otto Motors),

TIAGo (Photo by PAL Robotics), Dexterous Hand (Photo by Shadow Robot), HyQ2Max

(Photo by Istituto Italiano di Tecnologia), TALOS (Photo by PAL Robotics)

References

Adolfo Rodríguez Tsouroukdissian. n.d. “[ROSCon2014] Ros_control: An Overview.”

https://vimeo.com/107507546.

Andersen, Thomas Timm. 2015. “Optimizing the Universal Robots ROS Driver.” Techni-

cal University of Denmark, Department of Electrical Engineering; http://orbit.dtu.dk/en/

publications/optimizing-the-universal-robots-ros-driver(20dde139-7e87-4552-8658-dbf2cdaab24b)

.html.

Badger, Julia, Dustin Gooding, Kody Ensley, Kimberly Hambuchen, and Allison Thack-

ston. 2016. “ROS in Space: A Case Study on Robonaut 2.” In Robot Operating System

(ROS), 343–73. Springer. doi:10.1007/978-3-319-26054-9_13.

Chitta, Sachin, Ioan Sucan, and Steve Cousins. 2012. “Moveit![ROS Topics].” IEEE

Robotics & Automation Magazine 19 (1). IEEE: 18–19.

Hart, Stephen, Paul Dinh, John D Yamokoski, Brian Wightman, and Nicolaus Radford.

2014. “Robot Task Commander: A Framework and IDE for Robot Application Develop-

ment.” In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

2014), 1547–54. IEEE. doi:10.1109/IROS.2014.6942761.

Lages, Walter Fetter. 2017. “Parametric Identication of the Dynamics of Mobile Robots

and Its Application to the Tuning of Controllers in ROS.” In Robot Operating System

(ROS), 191–229. Springer. doi:10.1007/978-3-319-54927-9_6.

Meier, Martin, Guillaume Walck, Robert Haschke, and Helge J Ritter. 2016. “Dis-

tinguishing Sliding from Slipping During Object Pushing.” In IEEE/RSJ Interna-

tional Conference on Intel ligent Robots and Systems (IROS 2016), 5579–84. IEEE.

doi:10.1109/IROS.2016.7759820.

“New Universal Robots Driver Makes Manipulation Research Easier.” n.d. https://www.

clearpathrobotics.com/2016/02/new-universal-robots-driver-makes-manipulation-easier.

Quigley, Morgan, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob

Chitta et al., (2017). ros_control: A generic and simple control framework for ROS. Journal of Open Source Software, 2(20), 456,

doi:10.21105/joss.00456

Wheeler, and Andrew Y Ng. 2009. “ROS: An Open-Source Robot Operating System.”

In ICRA Workshop on Open Source Software, 3:5. 3.2. Kobe; http://www.willowgarage.

com/papers/ros-open-source-robot-operating-system.

Radford, Nicolaus A., Philip Strawser, Kimberly Hambuchen, Joshua S. Mehling,

William K. Verdeyen, A. Stuart Donnan, James Holley, et al. 2015. “Valkyrie:

NASA’s First Bipedal Humanoid Robot.” Journal of Field Robotics 32 (3): 397–419.

doi:10.1002/rob.21560.

Semini, Claudio, Victor Barasuol, Jake Goldsmith, Marco Frigerio, Michele Focchi, Yifu

Gao, and Darwin G Caldwell. 2017. “Design of the Hydraulically Actuated, Torque-

Controlled Quadruped Robot HyQ2Max.” IEEE/ASME Transactions on Mechatronics

22 (2). IEEE: 635–46. doi:10.1109/TMECH.2016.2616284.

Semini, Claudio, Nikos G Tsagarakis, Emanuele Guglielmino, Michele Focchi, Ferdinando

Cannella, and Darwin G Caldwell. 2011. “Design of HyQ–a Hydraulically and Electrically

Actuated Quadruped Robot.” Proceedings of the Institution of Mechanical Engineers,

Part I: Journal of Systems and Control Engineering 225 (6). SAGE Publications Sage

UK: London, England: 831–49. doi:10.1109/IROS.2010.5651548.

Stasse, Olivier, Thomas Flayols, Rohan Budhiraja, Kevin Giraud-Esclasse, Justin Car-

pentier, Andrea Del Prete, Philippe Soueres, et al. 2017. “TALOS: A New Humanoid

Research Platform Targeted for Industrial Applications.” https://hal.archives-ouvertes.

fr/hal-01485519.

Willow Garage. 2009. “Universal Robot Description Format (URDF).” http://wiki.ros.

org/urdf/.

Chitta et al., (2017). ros_control: A generic and simple control framework for ROS. Journal of Open Source Software, 2(20), 456,

doi:10.21105/joss.00456

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