This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are prone to rolling over at low speeds and at small articulation angles. A bicycle model with a nonlinear tire model was used as a vehicle model for AERS. Using that vehicle model, path tracking and speed controllers were designed in order to follow a predefined path and speed profile, respectively. To check the rollover propensity of AERS, load transfer ratio (LTR) based the rollover analysis was completed. Based on the results of the analysis, a rollover prevention scheme was proposed. To validate the proposed scheme, a simulation was conducted using a U-shaped path under constant speed conditions. From the simulation, it was shown that the proposed scheme is effective in preventing AERS from rolling over.

This paper presents a model predictive control (MPC)-based algorithm for rollover prevention of an autonomous electric road sweeper (AERS). For AERS, the basic function of autonomous driving is a path- and velocity-tracking control needed to make a vehicle follow given path and velocity profiles. On the other, the AERS adopts an articulated frame steering (AFS) mechanism which can make cornering behavior agile. Moreover, the tread of the AERS is narrow, and the height of the mass center is high. As a result, it is prone to roll over. For this reason, it is necessary to design a controller for path and velocity tracking and rollover prevention in order to improve maneuverability and roll safety of the AERS. A kinematic model was adopted as a vehicle one for the AERS. With the vehicle model, reference states of position and velocity were determined that are needed to make the AERS track the reference path and prevent rollover. With the vehicle model and reference states, an MPC-based motion controller was designed to optimize articulation angle and velocity commands. The load-transfer ratio (LTR) was used to measure a rollover propensity. To evaluate the proposed algorithm, a simulation was conducted for the U-turn scenario. Simulation results show that the proposed algorithm improves path tracking and prevents the rollover of the AERS.