Model predictive control-based target search and tracking using autonomous mobile robot with limited sensing domain

Abstract

Target search and tracking using autonomous robots is important for both civilian and military applications. In this work, we propose a model predictive control (MPC)-based path planning approach for a ground mobile robot to autonomously search and track a moving target. The robot is equipped with a sensor with limited sensing domain (bounded sensing range and angle of view) for target detection. Both target motion and sensor measurement use linear time-invariant models. Due to the limited sensing domain, we utilize a modified Kalman filter to handle the intermittent measurements. Under the MPC framework, the sensing domain is approximated with a bell-shaped differentiable function and is explicitly considered in the optimization problem. To reduce the computation burden of solving MPC, we propose a two-step procedure: it first considers the limited sensing range and computes a reference trajectory, which is then used for solving the original MPC that considers both limited sensing range and angle. The effectiveness of the proposed method is demonstrated by numerical simulations.