Multi-Robot Autonomous Navigation System Using Informational Fault Tolerant Multi-Sensor Fusion with Robust Closed Loop Sliding Mode Control

This paper presents a strategy to combine a fault tolerant multi-sensor data fusion with a closed loop controller scheme robust against external disturbances applied to a multi-robot mobile system tracking different trajectories. Multi-sensor fusion is ensured using an information filter which is the canonical form of the Kalman filter. Fault detection and exclusion strategy is proposed to eliminate any erroneous measurements, for this purpose, risiduals are generated using the Kullback-Leibler diveregence that compares the priori and posteriori distributions of the predicted and the corrected estimations respectively. Prediction model is based on odometry, with encoders data as input. Observation model is based on extra sensors observations. To optimise detections, an adaptive thresholding method based on the Kullback-Leibler Criteron is proposed. Trajectory tracking is achieved using a sliding mode controller (SMC), developped by inverting the dynamical model of mobile robots. SMC is robust against external matched disturbances, parameters variation and actuators detoriation, however it can not handle total loss of effectiveness, which makes detection and isolation of faulty actuators compulsory. For this purpose, controllers inputs are converted into expected elementary velocities and compared to real data obtained from fault tolerant multi-sensor data fusion. The main contribution of this paper is to combine a fault detection and exclusion (FDE) scheme with an enhanced sliding mode controller in order to detect and isolate both sensors and actuators faults. The method is applied to a multi-robot system. The obtained experimental results demonstrate the egibility and the effectiveness of the proposed approach.