Applying dynamic reconfiguration in the mobile robotics domain: A case study on computer vision algorithms

Mobile robots are widely used in industrial environments and are expected to be widely available in human environments in the near future, for example, in the area of care and service robots. This article proposes an implementation for a highly customizable color recognition module based on Field Programmable Gate Array (FPGA) hardware to accomplish tasks like real-time frame processing for image streams. In comparison to a pure software solution on a CPU, an attached FPGA-based hardware accelerator enables real-time image processing and significantly reduces the required computing power of the CPU. Instead, the CPU can be used for tasks that cannot be efficiently implemented on FPGAs, for example, because of a large control overhead. We concentrate on a multirobot scenario where a group of robots follows a human team member by keeping a specific formation in order to support the human in exploration and object detection. Additionally, the robots provide a communication infrastructure to maintain a stable multihop communication network between the human and a base station recording all actions and evaluating the captured images and transmitted data. Depending on the current operating conditions, the robot system has to be able to execute a wide variety of different tasks. Since only a small number of tasks have to be executed concurrently, dynamic reconfiguration of the FPGA can be used to avoid the parallel implementation of all tasks on the FPGA. Within this context, this article discusses application fields where dynamic reconfiguration of FPGA-based coprocessors significantly reduces the CPU load and presents examples of how dynamic reconfiguration can be used in exploration.

[1]  Alex M. Andrew Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems , 2004 .

[2]  Thomas Bräunl Embedded robotics - mobile robot design and applications with embedded systems (2. ed.) , 2003 .

[3]  Wayne Luk,et al.  Design optimizations to improve placeability of partial reconfiguration modules , 2009, 2009 Design, Automation & Test in Europe Conference & Exhibition.

[4]  P.M. Athanas,et al.  Real-Time Image Processing on a Custom Computing Platform , 1995, Computer.

[5]  V. Braitenberg Vehicles, Experiments in Synthetic Psychology , 1984 .

[6]  Jong-Hwan Kim,et al.  Multi-Layer Architecture of Ubiquitous Robot System for Integrated Services , 2009, Int. J. Soc. Robotics.

[7]  Tobias Becker,et al.  Modular dynamic reconfiguration in Virtex FPGAs , 2006 .

[8]  Jack Koplowitz,et al.  The weighted nearest neighbor rule for class dependent sample sizes (Corresp.) , 1979, IEEE Trans. Inf. Theory.

[9]  Jeffrey L. Krichmar,et al.  Neuromodulation as a robot controller , 2009, IEEE Robotics & Automation Magazine.

[10]  Lars Braun,et al.  Adaptive real-time image processing exploiting two dimensional reconfigurable architecture , 2009, Journal of Real-Time Image Processing.

[11]  Ulf Witkowski,et al.  Parallel Early Vision Algorithms for Mobile Robots , 2007 .

[12]  Miguel Arias-Estrada,et al.  Hardware/Software FPGA Architecture for Robotics Applications , 2009, ARC.

[13]  Mario Porrmann,et al.  A Design Methodology for Communication Infrastructures on Partially Reconfigurable FPGAs , 2007, 2007 International Conference on Field Programmable Logic and Applications.

[14]  Florian Dittmann,et al.  Hard Real-Time Reconfiguration Port Scheduling , 2007, 2007 Design, Automation & Test in Europe Conference & Exhibition.

[15]  Ulrich Rückert,et al.  Visual Object Recognition by 2D-Color Camera and On-Board Information Processing for Minirobots , 2004 .

[16]  H. B. Mitchell,et al.  Multi-Sensor Data Fusion: An Introduction , 2007 .

[17]  Manuela M. Veloso,et al.  Fast and inexpensive color image segmentation for interactive robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[18]  A. Lynn Abbott,et al.  Image Processing on a Custom Computing Platform , 1994, FPL.

[19]  Jean-Didier Legat,et al.  An Evaluation of Dynamic Partial Reconfiguration for Signal and Image Processing in Professional Electronics Applications , 2008, EURASIP J. Embed. Syst..

[20]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[21]  Mario Porrmann,et al.  Component Case Study of a Self-Optimizing RCOS/RTOS System , 2005, IESS.

[22]  Lyuba Alboul,et al.  Ad-hoc Network Communication Infrastructure for Multi-robot Systems in Disaster Scenarios , 2008 .

[23]  Donald G. Bailey,et al.  User evaluation and overview of a visual language for real time image processing on FPGAs , 2009, CHINZ '09.

[24]  Wayne Luk,et al.  Design Optimizations for Tiled Partially Reconfigurable Systems , 2011, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[25]  Yoshiki Yamaguchi,et al.  Dynamic reconfiguration system for real-time video processing , 2009, 2009 International Conference on Field Programmable Logic and Applications.

[26]  Fabienne Nouvel,et al.  Partial and dynamic reconfiguration of FPGAs: a top down design methodology for an automatic implementation , 2006, IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures (ISVLSI'06).

[27]  Ranga Vemuri,et al.  iPACE-V1: A Portable Adaptive Computing Engine for Real Time Applications , 2002, FPL.

[28]  SciutoDonatella,et al.  Applying dynamic reconfiguration in the mobile robotics domain , 2011 .

[29]  Henry Hoffmann,et al.  Self-Aware Adaptation in FPGA-based Systems , 2010, 2010 International Conference on Field Programmable Logic and Applications.

[30]  Chun-Hsian Huang,et al.  Reconfigurable System Design and Verification , 2009 .

[31]  Sesh Commuri,et al.  Task-based Hardware Reconfiguration in Mobile Robots Using FPGAs , 2007, J. Intell. Robotic Syst..

[32]  Ulrich Rückert,et al.  Robot Localization based on Visual Landmarks , 2008, ICINCO-RA.

[33]  Thia Kirubarajan,et al.  Estimation and Decision Fusion: A Survey , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[34]  Carla E. Brodley,et al.  Multivariate decision trees , 2004, Machine Learning.

[35]  Walter Stechele,et al.  Towards Rapid Dynamic Partial Reconfiguration in Video-Based Driver Assistance Systems , 2010, ARC.

[36]  James K. Archibald,et al.  Reconfigurable On-Board Vision Processing for Small Autonomous Vehicles , 2007, EURASIP J. Embed. Syst..

[37]  P. Utgoff,et al.  Multivariate Decision Trees , 1995, Machine Learning.