The stability of industrial vehicles, such as forklifts and lifters, is very important from a safety point of view: these vehicles are subjected to variable loading conditions and their design is often optimized to privilege handling in narrow spaces, instead of stability. To study in deep the problem of vehicle capsize prevention, the authors developed a scaled AGV (Automated Guided Vehicle). It is a three wheeled differential drive mobile robot, with front motorized wheels, driven by speed-controlled drivers. The position of the wheels and the loads can be easily modified to simulate different vehicle configurations and operating scenarios. The vehicle is controlled with a Texas Instrument C2000 controller, programmable through Matlab-Simulink Embedded Coder™. In addition, the forces exchanged among the wheels and the ground are monitored using low cost load cells, with miniaturized amplification stages. MEMS three-axial accelerometers and gyros are installed in order to detect inertial loads and to estimate the vehicle pose and, through a proper filtering, the ground slope. The implemented strategy is able to identify the loading conditions of the vehicle by means of a dedicated algorithm: this algorithm evaluates the position of the center of mass from static measurements that are further refined when the vehicle is in motion with an adaptive filtering based on the fusion of both static and dynamic measurements. Once the vehicle is in motion, the controller, to prevent the vehicle capsize, is able to limit its forward speed without changing the geometry of the assigned trajectory. In this work, the results of preliminary testing activities are shown, demonstrating the validity and the effectiveness of the proposed approach.
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