Robot Navigation in a Decentralized Landmark-Free Sensor Network
Journal of Intelligent and Robotic Systems
| Hi-index | 0.01 |
When an emergency occurs within a building, it is safer to send autonomous mobile agents instead of human responders, to explore the area and identify hazards and victims. Existing exploration algorithms (Svennebring, J. and Koenig, S. (2004) Building terrain-covering Ant robots: a feasibility study. Auton. Robots, 16, 313–332, Ferranti, E., Trigoni, N. and Levene, M. (2007) Brick&Mortar: An On-Line Multi-Agent Exploration Algorithm. ICRA07: Proc. 2007 IEEE Int. Conf. Robotics and Automation, April, pp. 761–767. IEEE Press) allow mobile agents to make distributed navigation decisions by communicating with nearby fixed sensors embedded in the environment. These algorithms are very efficient in terms of exploration time, but they have only been evaluated in simulation environments, where idealized assumptions were made regarding the ability of agents to localize sensors and move accurately towards them. The objective of this work is to investigate practical issues of building a real testbed of mobile agents and fixed sensors, and implementing exploration algorithms in such a testbed. In particular, we describe our experiences from building a real system consisting of a Surveyor SRV-1 robot and Tmote Sky sensors running the Contiki OS (Dunkels, A., Gron̈vall, B. and Voigt, T. (2004) Contiki — A Lightweight and Flexible Operating System for Tiny Networked Sensors. Proc. 1st Annual IEEE Int. Workshop on Embedded Networked Sensors, November, pp. 455–462). We select two existing exploration algorithms, Ants (Svennebring, J. and Koenig, S. (2004) Building terrain-covering Ant robots: a feasibility study. Auton. Robots, 16, 313–332) and Brick&Mortar (Ferranti, E., Trigoni, N. and Levene, M. (2007) Brick&Mortar: An On-Line Multi-Agent Exploration Algorithm. ICRA07: Proc. 2007 IEEE Int. Conf. Robotics and Automation, April, pp. 761–767. IEEE Press), and discuss challenges in trying to implement them in our testbed. To address these challenges, we propose practical solutions that allow a mobile agent to: (i) identify and localize fixed sensors deployed in its vicinity and (ii) accurately move towards a carefully selected fixed sensor. Using our real network deployment, we derive realistic models of localization and odometry errors. We then insert these error models into a realistic simulation environment, in order to extensively compare Ants and Brick&Mortar, and measure their performance degradation as a result of introducing realistic errors.