Abstract
Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.
Highlights
Robots have become increasingly prevalent in many domains, such as medical services and security [1]
The localization results obtained for the robot team using the split covariance intersection filter cooperative localization (SCIFCL) algorithm and the SCIFCL
An fault detection and isolation (FDI) algorithm is applied in decentralized cooperative localization (DCL) for a multirobot system
Summary
Robots have become increasingly prevalent in many domains, such as medical services and security [1]. Tasks assigned to robots such as rescue [2] are becoming increasingly complex for a single robot to address, making multirobot cooperation imperative. The cooperation intrinsic to a multirobot team makes it possible to execute tasks such as rescue which may be difficult or difficult with a single, highly capable robot; the presence of more than one robot makes the team fault-tolerant. The localization process is performed based only on its own sensor data, such as Global. Each robot is assumed to be equipped with one motion sensor. The equipped sensors can report the motion data on of the robots. No single-robot localization method based on other sensors is considered in these simulations. A robot that is perceived is called a neighboring robot in this paper. Each robot can share its relative measurements and motion states with its neighboring robots via inter-robot communication
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