Abstract
In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then the state equation and measure equation were reconstructed by introducing the communication delays into the states and measurements. Furthermore, the cooperative localization algorithm using the extended Kalman filtering technique based on state estimation error compensation was proposed to reduce the state estimation error of delay filtering. Finally, the simulation and experiment results demonstrated that the proposed algorithm can achieve good performance in location in the presence of communication delay while having reduced computational and communicative cost.
Highlights
In recent years, multi-robot systems have received widespread attention as a team of robots could increase reliability and performance when compared to an individual robot
A cooperative localization method based on the extended Kalman filter (EKF) was proposed in [13], where the position and posture information of each robot measured by the sensor was updated in time, and the entropic criterion was utilized to ensure that the optimal measurement was used to reduce the uncertainty of the robot pose estimation
Based on the measurement update, this paper proposed a delayed extended Kalman filter (DEKF) to deal with the problem of cooperative localization with communication delays
Summary
Multi-robot systems have received widespread attention as a team of robots could increase reliability and performance when compared to an individual robot. A cooperative localization method based on the extended Kalman filter (EKF) was proposed in [13], where the position and posture information of each robot measured by the sensor was updated in time, and the entropic criterion was utilized to ensure that the optimal measurement was used to reduce the uncertainty of the robot pose estimation. In order to solve this problem, many methods have been proposed, and the commonly used method to solve information delay is to use robust theory to predict and compensate the random delay model These methods are generally used in complex network systems with more sensors, shorter time delay, and disorderly measurement [20,21,22,23,24,25,26,27,28]. The proposed cooperative localization algorithm is applied to the leader–follower robot formation to demonstrate the effectiveness of the proposed strategy
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