Date of this Version
John R. Spletzer and Camillo J. Taylor, "A Bounded Uncertainty Approach to Multi-Robot Localization", . October 2003.
We offer a new approach to the multi-robot localization problem. Using an unknown-but-bounded model for sensor error, we are able to define convex polytopes in the configuration space of the robot team that represent the set of configurations consistent with all sensor measurements. Estimates for the uncertainty in various parameters of the team's configuration such as the absolute position of a single robot, or the relative positions of two or more nodes can be obtained by projecting this polytope onto appropriately chosen subspaces of the configuration space. We propose a novel approach to approximating these projections using linear programming techniques.
The approach can handle both bearing and range measurements with a computational complexity scaling polynomially in the number of robots. Finally, the workload is readily distributed - requiring only the communication of sensor measurements between robots. We provide simulation results for this approach implemented on a multi-robot team.
computational complexity, cooperative systems, linear programming, multi-robot systems, position control, sensor fusion, bearing measurement, bounded uncertainty approach, computational complexity, configuration space, convex polytopes, linear programming techniques, multirobot localization, multirobot team, range measurements, sensor error, sensor measurements, uncertainty estimation
Date Posted: 17 November 2004
This document has been peer reviewed.