A Bounded Uncertainty Approach to Multi-Robot Localization
Penn collection
Degree type
Discipline
Subject
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
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Contributor
Abstract
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.