research-article

ReconfROS: Running ROS on Reconfigurable SoCs

Authors:

Steffen Hinderink,

Marco Tassemeier,

Marcel Flottmann,

Thomas Wiemann,

Mario PorrmannAuthors Info & Claims

DroneSE and RAPIDO '21: Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings

Pages 16 - 21

https://doi.org/10.1145/3444950.3444959

Published: 24 February 2021 Publication History

Abstract

In this paper, we present an approach to integrate reconfigurable SoCs into the well known Robot Operating System (ROS). Our method allows to implement hardware-accelerated algorithms on FPGA and integrate them directly into the ROS ecosystem. This allows to combine the established and well tested ROS infrastructure together with low-power hardware acceleration. As a proof-of-concept for this novel integration, we ported an existing path-following algorithm onto an FPGA and tested it on an unmanned ground vehicle (UGV).

References

[1]

Andreas Bartel, Frank Meyer, Christopher Sinke, Thomas Wiemann, Andreas Nüchter, Kai Lingemann, and Joachim Hertzberg. 2007. Real-time outdoor trail detection on a mobile robot. In Proceedings of the 13th IASTED International Conference on Robotics, Applications and Telematics. 477–482.

[2]

Konstantinos Boikos and Christos Savvas Bouganis. 2016. Semi-dense SLAM on an FPGA SoC. In FPL 2016 - 26th International Conference on Field-Programmable Logic and Applications. Institute of Electrical and Electronics Engineers Inc., 1–4. https://doi.org/10.1109/FPL.2016.7577365

[3]

Haoxuan Cheng, Shimpei Sato, and Hiroki Nakahara. 2018. A Performance Per Power Efficient Object Detector on an FPGA for Robot Operating System (ROS). In Proceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies (Toronto, ON, Canada) (HEART 2018). Association for Computing Machinery, New York, NY, USA, Article 20, 4 pages. https://doi.org/10.1145/3241793.3241814

Digital Library

[4]

Luis Contreras, Sergio Cruz, J. M.S.T. Motta, and Carlos H. Llanos. 2016. FPGA implementation of the EKF algorithm for localization in mobile robotics using a unified hardware module approach. In 2015 International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015. Institute of Electrical and Electronics Engineers Inc., 1–6. https://doi.org/10.1109/ReConFig.2015.7393315

[5]

Sergio Cruz, Daniel M. Munoz, Milton Conde, Carlos H. Llanos, and Geovany A. Borges. 2013. FPGA implementation of a sequential Extended Kalman Filter algorithm applied to mobile robotics localization problem. In 2013 IEEE 4th Latin American Symposium on Circuits and Systems, LASCAS 2013 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 1–4. https://doi.org/10.1109/LASCAS.2013.6519021

[6]

K. Hasegawa, K. Takasaki, M. Nishizawa, R. Ishikawa, K. Kawamura, and N. Togawa. 2019. Implementation of a ROS-Based Autonomous Vehicle on an FPGA Board. In International Conference on Field-Programmable Technology (ICFPT). IEEE, Tianjin, China, 457–460.

[7]

Qunfang He, · Wenjie Chen, Danping Zou, · Zhilei Chai, and Zhilei Chai. 2020. A novel framework for UAV returning based on FPGA. The Journal of Supercomputing(2020). https://doi.org/10.1007/s11227-020-03434-4

[8]

Erwan Moréac, El Mehdi Abdali, François Berry, Dominique Heller, and Jean-Philippe Diguet. 2020. Hardware-in-the-loop simulation with dynamic partial FPGA reconfiguration applied to computer vision in ROS-based UAV. In 31st International Workshop on Rapid System Prototyping (RSP). Virtual Conference (ESWEEK), France. https://hal.archives-ouvertes.fr/hal-02948474

[9]

Y. Nitta, S. Tamura, and H. Takase. 2018. A Study on Introducing FPGA to ROS Based Autonomous Driving System. In 2018 International Conference on Field-Programmable Technology (FPT). IEEE, Naha, Okinawa, Japan, 421–424.

[10]

Y. Nitta, S. Tamura, H. Yugen, and H. Takase. 2019. ZytleBot: FPGA Integrated Development Platform for ROS Based Autonomous Mobile Robot. In International Conference on Field-Programmable Technology (ICFPT). IEEE, Barcelona, Spain, 445–448. https://doi.org/10.1109/ICFPT47387.2019.00089

[11]

T. Ohkawa, Y. Sugata, H. Watanabe, N. Ogura, K. Ootsu, and T. Yokota. 2019. High Level Synthesis of ROS Protocol Interpretation and Communication Circuit for FPGA. In 2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering (RoSE). IEEE Computer Society, 33–36.

[12]

Takeshi Ohkawa, Kazushi Yamashina, Hitomi KIMURA, Kanemitsu Ootsu, and Takashi YOKOTA. 2018. FPGA components for integrating FPGAs into robot systems. IEICE Transactions on Information and Systems E101.D (02 2018), 363–375. https://doi.org/10.1587/transinf.2017RCP0011

[13]

Takeshi Ohkawa, Kazushi Yamashina, Takuya Matsumoto, Kanemitsu Ootsu, and Takashi Yokota. 2016. Architecture Exploration of Intelligent Robot System Using ROS-Compliant FPGA Component. In Proceedings of the 27th International Symposium on Rapid System Prototyping: Shortening the Path from Specification to Prototype (Pittsburgh, Pennsylvania) (RSP ’16). Association for Computing Machinery, New York, NY, USA, 72–78. https://doi.org/10.1145/2990299.2990312

Digital Library

[14]

A. Podlubne and D. Göhringer. 2019. FPGA-ROS: Methodology to Augment the Robot Operating System with FPGA Designs. In International Conference on ReConFigurable Computing and FPGAs (ReConFig). IEEE, Cancun, Mexico, 1–5. https://doi.org/10.1109/ReConFig48160.2019.8994719

[15]

Murad Qasaimeh, Joseph Zambreno, Phillip H. Jones, Kristof Denolf, Jack Lo, and Kees Vissers. 2019. Analyzing the energy-efficiency of vision kernels on embedded cpu, GPU and FPGA platforms. In Proceedings - 27th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019. Institute of Electrical and Electronics Engineers Inc., 336. https://doi.org/10.1109/FCCM.2019.00077

[16]

Santiago Sánchez-Solano, Alejandro J. Cabrera, Iluminada Baturone, Francisco J. Moreno-Velo, and María Brox. 2007. FPGA implementation of embedded fuzzy controllers for robotic applications. IEEE Transactions on Industrial Electronics 54, 4 (aug 2007), 1937–1945. https://doi.org/10.1109/TIE.2007.898292

[17]

Tahiyah Nou Shene, K. Sridharan, and N. Sudha. 2016. Real-Time SURF-Based Video Stabilization System for an FPGA-Driven Mobile Robot. IEEE Transactions on Industrial Electronics 63, 8 (aug 2016), 5012–5021. https://doi.org/10.1109/TIE.2016.2551684

[18]

Xuesong Shi, Lu Cao, Dawei Wang, Ling Liu, Ganmei You, Shuang Liu, and Chunjie Wang. 2018. HERO: Accelerating Autonomous Robotic Tasks with FPGA. In IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 7766–7772. https://doi.org/10.1109/IROS.2018.8593522

[19]

Yuhei Sugata, Takeshi Ohkawa, Kanemitsu Ootsu, and Takashi Yokota. 2017. Acceleration of Publish/Subscribe Messaging in ROS-Compliant FPGA Component. In Proceedings of the 8th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (Bochum, Germany) (HEART2017). Association for Computing Machinery, New York, NY, USA, Article 13, 6 pages. https://doi.org/10.1145/3120895.3120904

Digital Library

[20]

Keisuke Sugiura and Hiroki Matsutani. 2020. An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm. arxiv:2006.01050 [eess.SP]

[21]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 07-12-June-2015. IEEE Computer Society, 1–9. https://doi.org/10.1109/CVPR.2015.7298594 arxiv:1409.4842

[22]

Zishen Wan, Bo Yu, Thomas Yuang Li, Jie Tang, Yuhao Zhu, Yu Wang, Arijit Raychowdhury, and Shaoshan Liu. 2020. A Survey of FPGA-Based Robotic Computing. arxiv:2009.06034 [cs.RO]

[23]

Ying Wang, Jie Xu, Yinhe Han, Huawei Li, and Xiaowei Li. 2016. DeepBurning: Automatic Generation of FPGA-Based Learning Accelerators for the Neural Network Family. In Proceedings of the 53rd Annual Design Automation Conference (Austin, Texas) (DAC ’16). Association for Computing Machinery, New York, NY, USA, Article 110, 6 pages. https://doi.org/10.1145/2897937.2898003

Digital Library

[24]

Thomas Wisspeintner, Walter Nowak, and Ansgar Bredenfeld. 2006. VolksBot – A Flexible Component-Based Mobile Robot System. In RoboCup 2005: Robot Soccer World Cup IX, Ansgar Bredenfeld, Adam Jacoff, Itsuki Noda, and Yasutake Takahashi (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 716–723.

[25]

K. Yamashina, H. Kimura, T. Ohkawa, K. Ootsu, and T. Yokota. 2016. cReComp: Automated Design Tool for ROS-Compliant FPGA Component. In 2016 IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC). 138–145. https://doi.org/10.1109/MCSoC.2016.47

[26]

Kazushi Yamashina, Takeshi Ohkawa, Kanemitsu Ootsu, and Takashi Yokota. 2015. Proposal of ROS-compliant FPGA Component for Low-Power Robotic Systems. arxiv:1508.07123 [cs.AR]

[27]

Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen mei Hwu, and Deming Chen. 2020. SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems. arxiv:1909.09709 [cs.CV]

Cited By

Mayoral-Vilches VJabbour JHsiao YWan ZCrespo-Álvarez MStewart MReina-Muñoz JNagras PVikhe GBakhshalipour MPinzger MRass SPanigrahi SCorradi GRoy NGibbons PNeuman SPlancher BReddi V(2024)RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610841(8288-8297)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610841
Lienen CNowosad PPlatzner M(2023)Mapping and Optimizing Communication in ROS 2-based Applications on Configurable System-on-Chip PlatformsProceedings of the 2023 9th International Conference on Robotics and Artificial Intelligence10.1145/3637843.3637846(23-31)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3637843.3637846
Bell OGill CZhang X(2023)Hardware Acceleration with Zero-Copy Memory Management for Heterogeneous Computing2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA58653.2023.00013(28-37)Online publication date: 30-Aug-2023
https://doi.org/10.1109/RTCSA58653.2023.00013
Show More Cited By

Recommendations

Acceleration of Publish/Subscribe Messaging in ROS-compliant FPGA Component
HEART '17: Proceedings of the 8th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies

Intelligent robots demand complex information processing such as SLAM (Simultaneous Localization and Mapping) and DNN (Deep Neural Network). FPGA (Field Programmable Gate Array) is expected to accelerate these applications with high energy efficiency. ...
ReconfROS: An approach for accelerating ROS nodes on reconfigurable SoCs
Abstract
In this paper, we present an approach to integrate reconfigurable SoCs into the well known Robot Operating System (ROS) called ReconfROS. Our method allows to implement hardware-accelerated algorithms on FPGAs and integrates them ...
DBHI: A Tool for Decoupled Functional Hardware-Software Co-Design on SoCs
FPGA '20: Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

This paper presents a system-level co-simulation and co-verification workflow to ease the transition from a software-only procedure, executed in a General Purpose processor, to the integration of a custom hardware accelerator developed in a Hardware ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

DroneSE and RAPIDO '21: Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings

January 2021

73 pages

ISBN:9781450389525

DOI:10.1145/3444950

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

DroneSE and RAPIDO '21

DroneSE and RAPIDO '21: Methods and Tools

January 18 - 20, 2021

Budapest, Hungary

Acceptance Rates

Overall Acceptance Rate 14 of 28 submissions, 50%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
201
Total Downloads

Downloads (Last 12 months)39
Downloads (Last 6 weeks)4

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mayoral-Vilches VJabbour JHsiao YWan ZCrespo-Álvarez MStewart MReina-Muñoz JNagras PVikhe GBakhshalipour MPinzger MRass SPanigrahi SCorradi GRoy NGibbons PNeuman SPlancher BReddi V(2024)RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610841(8288-8297)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610841
Lienen CNowosad PPlatzner M(2023)Mapping and Optimizing Communication in ROS 2-based Applications on Configurable System-on-Chip PlatformsProceedings of the 2023 9th International Conference on Robotics and Artificial Intelligence10.1145/3637843.3637846(23-31)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3637843.3637846
Bell OGill CZhang X(2023)Hardware Acceleration with Zero-Copy Memory Management for Heterogeneous Computing2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA58653.2023.00013(28-37)Online publication date: 30-Aug-2023
https://doi.org/10.1109/RTCSA58653.2023.00013
Lienen CMiddeke SPlatzner M(2023)FPGADDS: An Intra-FPGA Data Distribution Service for ROS 2 Robotics Applications2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10341921(6261-6266)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10341921
Lienen CBrede MKarger DKoch KLogan DMazur JNowosad ASchnelle AWaizy MPlatzner M(2023)AutonomROS: A ReconROS-based Autonomous Driving Unit2023 Seventh IEEE International Conference on Robotic Computing (IRC)10.1109/IRC59093.2023.00056(297-304)Online publication date: 11-Dec-2023
https://doi.org/10.1109/IRC59093.2023.00056
Podlubne AGöhringer D(2023)A Survey on Adaptive Computing in Robotics: Modelling, Methods and ApplicationsIEEE Access10.1109/ACCESS.2023.328119011(53830-53849)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3281190
Machado FNieto RFernández-Conde JLobato DCañas J(2023)Vision-based robotics using open FPGAsMicroprocessors and Microsystems10.1016/j.micpro.2023.104974103(104974)Online publication date: Nov-2023
https://doi.org/10.1016/j.micpro.2023.104974
Bernardi ABrilli GCapotondi AMarongiu ABurgio P(2022)An FPGA Overlay for Efficient Real-Time Localization in 1/10th Scale Autonomous Vehicles2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE54114.2022.9774517(915-920)Online publication date: 14-Mar-2022
https://doi.org/10.23919/DATE54114.2022.9774517
Lienen CPlatzner M(2022)Design of Distributed Reconfigurable Robotics Systems with ReconROSACM Transactions on Reconfigurable Technology and Systems10.1145/349457115:3(1-20)Online publication date: 30-Sep-2022
https://dl.acm.org/doi/10.1145/3494571
Mayoral-Vilches VNeuman SPlancher BReddi V(2022)RobotCore: An Open Architecture for Hardware Acceleration in ROS 22022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS47612.2022.9982082(9692-9699)Online publication date: 23-Oct-2022
https://doi.org/10.1109/IROS47612.2022.9982082
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents