Department of Computer, Control and Management Engineering

We present a novel method for mobile robot navigation among obstacles. Our approach is based on Nonlinear Model Predictive Control (NMPC) and uses a dynamics-aware collision avoidance constraint. The constraint, built upon the notion of avoidable collision state, considers not only the robot-obstacle distance but also their velocity as well as the robot actuation capabilities. To highlight the effectiveness of this constraint, we compare the proposed method with a version of the NMPC that uses a constraint purely based on distance information, showing that the first achieves better performance than the second, especially when the robot travels at higher speed among several moving obstacles. Results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.