  Autonomous ground vehicles (AGVs) are receiving increasing attention, and the
motion planning and control problem for these vehicles has become a hot
research topic. In real applications such as material handling, an AGV is
subject to large uncertainties and its motion planning and control become
challenging. In this paper, we investigate this problem by proposing a
hierarchical control scheme, which is integrated by a model predictive control
(MPC) based path planning and trajectory tracking control at the high level,
and a reduced-order extended state observer (RESO) based dynamic control at the
low level. The control at the high level consists of an MPC-based improved path
planner, a velocity planner, and an MPC-based tracking controller. Both the
path planning and trajectory tracking control problems are formulated under an
MPC framework. The control at the low level employs the idea of active
disturbance rejection control (ADRC). The uncertainties are estimated via a
RESO and then compensated in the control in real-time. We show that, for the
first-order uncertain AGV dynamic model, the RESO-based control only needs to
know the control direction. Finally, simulations and experiments on an AGV with
different payloads are conducted. The results illustrate that the proposed
hierarchical control scheme achieves satisfactory motion planning and control
performance with large uncertainties.
