A multiple sensor fusion based drift compensation algorithm for mecanum wheeled mobile robots

Abstract

This paper investigates a multiple sensor fusion based drift compensation technique for a mecanum wheeled mobile robot platform. The mobile robot is equipped with high precision encoders integrated to the wheels and four accelerometers placed on its chassis. The proposed algorithm combines the information from the encoders and the acceleration sensors to estimate the total drift in the acceleration dimension. The inner loop controller is designed utilizing a disturbance observer based acceleration control structure which is blind against the slipping motion of the wheels. The estimated drift acceleration from the sensor fusion is then mapped back to the joint space of the robot and used as additional compensation over the existing controllers. The proposed algorithm is tested on a series of experiments. The results of the experiments are also compared with a recent study in order to provide a benchmark evaluation. The enhanced tracking performance yielding towards smaller error magnitudes in the experiments illustrate the efficacy and success of the proposed control architecture in attenuating the positioning drift of mecanum wheeled robots.

Vision SLAM algorithm for wheeled robots integrating multiple sensors

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“…In Eq (10), there are the Mahalanobis distance measurement error terms for visual, IMU, and encoder, respectively, representing the marginalization term. H is the total number of maps for the Kth frame, P is the Huber robust kernel function, and E, EI, and EG are the noise covariance matrices for visual, IMU, and encoder, respectively, representing the state variables.…”

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

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