Mobile robotic systems are becoming increasingly popular. These systems are
used in various indoor applications, raging from warehousing and manufacturing
to test benches for assessment of advanced control strategies, such as
artificial intelligence (AI)-based control solutions, just to name a few.
Scaled robotic cars are commonly equipped with a hierarchical control
acthiecture that includes tasks dedicated to vehicle state estimation and
control. This paper covers both aspects by proposing (i) a federeted extended
Kalman filter (FEKF), and (ii) a novel deep reinforcement learning (DRL) path
tracking controller trained via an expert demonstrator to expedite the learning
phase and increase robustess to the simulation-to-reality gap. The paper also
presents the formulation of a vehicle model along with an effective yet simple
procedure for identifying tis paramters. The experimentally validated model is
used for (i) supporting the design of the FEKF and (ii) serving as a digital
twin for training the proposed DRL-based path tracking algorithm. Experimental
results confirm the ability of the FEKF to improve the estimate of the mobile
robot's position. Furthermore, the effectiveness of the DRL path tracking
strateguy is experimentally tested along manoeuvres not considered during
training, showing also the ability of the AI-based solution to outpeform
model-based control strategies and the demonstrator. The comparison with
benchmraking controllers is quantitavely evalueted through a set of key
performance indicators.

移动机器人系统中，提出了一种分散拓展的扩展卡尔曼滤波器和一种通过专家演示器训练的强化学习路径跟踪控制器的新颖方案，实验结果表明，FEKF 能够改善移动机器人位置的估计，AI 解决方案能够胜过基于模型的控制策略和演示器，同时通过一系列性能指标对比评估与基准控制器。