Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30124
Autonomic Sonar Sensor Fault Manager for Mobile Robots

Authors: Martin Doran, Roy Sterritt, George Wilkie

Abstract:

NASA, ESA, and NSSC space agencies have plans to put planetary rovers on Mars in 2020. For these future planetary rovers to succeed, they will heavily depend on sensors to detect obstacles. This will also become of vital importance in the future, if rovers become less dependent on commands received from earth-based control and more dependent on self-configuration and self-decision making. These planetary rovers will face harsh environments and the possibility of hardware failure is high, as seen in missions from the past. In this paper, we focus on using Autonomic principles where self-healing, self-optimization, and self-adaption are explored using the MAPE-K model and expanding this model to encapsulate the attributes such as Awareness, Analysis, and Adjustment (AAA-3). In the experimentation, a Pioneer P3-DX research robot is used to simulate a planetary rover. The sonar sensors on the P3-DX robot are used to simulate the sensors on a planetary rover (even though in reality, sonar sensors cannot operate in a vacuum). Experiments using the P3-DX robot focus on how our software system can be adapted with the loss of sonar sensor functionality. The autonomic manager system is responsible for the decision making on how to make use of remaining ‘enabled’ sonars sensors to compensate for those sonar sensors that are ‘disabled’. The key to this research is that the robot can still detect objects even with reduced sonar sensor capability.

Keywords: Autonomic, self-adaption, self-healing, self-optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131181

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 572

References:


[1] D. P. Miller, T. Hunt, M. Roman, S. Swindell, L. Tan and A. Winterholler, “Experiments With a Long-Range Planetary Rover,” University of Oklahoma Norman, OK, 73019 USA.
[2] D. M. Chess, A. Segal, I. Whalley, and S. R. White, “An architectural blueprint for autonomic computing,” IBM Corporation, 2004.
[3] T. Huntsberger, “Fault Tolerant Action Selection for Planetary Rover Control,” University of South Carolina, Columbia, SC 29208, USA.
[4] T. Kohler, E. Berghofer, “Sensor Fault Detection and Compensation in Lunar/Plantary Robot Missions,” University of Bremen, 28359, Germany
[5] M. Doran, R. Sterritt, G. Wilkie, “Self-Adaptive Wheel Alignment For Mobile Robots,” IARIA Conference, Rome, 2016.
[6] Adept Mobile Robots. Pioneer 3 Operations Manual, Version 6, 2010.
[7] Microsoft. Microsoft Robotics Developer Studio. (Online). Available from: http://www.microsoft.com/robotics/ (Accessed 10 September 2016).
[8] N. K. Melchior and W. D. Smart, “Autonomic Systems for Mobile Robots.” Department of Computer Science and Engineering, Washington University, MO, 63130 USA.
[9] D. Crestani, K. Godary-Dejean, “Fault Tolerance in Control Architectures for Mobile Robots: Fantasy or Reality?,” Laboratoire Informatique Robotique Microélectronique de Montpellier Université Montpellier Sud de France.
[10] M. K. Habib, “Real Time Mapping and Dynamic Navigation for Mobile Robots,” International Journal of Advanced Robotic Systems, Vol. 4, No. 3 (2007) ISSN 1729-8806, pp. 323-338.
[11] Sensor failure detection through introspection. (Online). Available from: http://hdl.handle.net/10945/3518 (Accessed 3 September 2016).
[12] E. Matson, S DeLoach, “Enabling Intra-Robotic Capabilities Adaptation Using An Organization-Based Multiagent System,” IEEE International Conference on Robotics and Automation (IEEE ICRA 04) on, May 2004, pp 2135-2140.
[13] O. Zweigle, B. Keil, M. Wittlinger, K. Haussermann and P. Levi, “Recognizing Hardware Faults on Mobile Robots Using Situation Analysis Techniques,” International Conference IAS-12 on, June 2012, pp 397-409.
[14] E. Khalastchi, M. Kalech, L. Rokach, Y Shicel and G. Bodek, “Sensor Fault Detection and Diagnosis for Autonomous Systems,” 22nd International Workshop on Principles of Diagnosis, October, 2011.
[15] Y. Dai, Y. Xiang and G. Zhang, “Self-healing and Hybrid Diagnosis in Cloud Computing,” DBLP Conference: Cloud Computing, First International Conference, CloudCom, December, 2009, pp. 45 – 56.
[16] M. Parashar and S. Hariri, “Autonomic Computing: an Overview,” Proceedings of the 2004 international conference on Unconventional Programming Paradigms, September 2004, pp. 257 – 269.
[17] M. Scheutz and J. Kramer, “Reflection and Reasoning Mechanisms for Failure Detection and Recovery in a Distributed Robotic Architecture for Complex Robots,” in Robotics and Automation, 2007 IEEE International Conference on, April 2007, pp. 3699-3704.
[18] P. Arcaini, E Riccobene and P Scandurra, “Modeling and Analyzing MAPE-K Feedback Loops for Self-adaptation,” Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, June 2015, pp. 13 – 23.
[19] Computerworld. IBM Adds Autonomic Tools to Speed Up Error Detection. (Online) Available from: http://www.computerworld.com/article/2557731/networking/ibm-adds-autonomic-tools-to-speed-up-error-detection.html (Accessed 27 September 2016).