  Compensating for slip and skid is crucial for mobile robots navigating
outdoor terrains. In these challenging environments, slipping and skidding
introduce uncertainties into trajectory tracking systems, potentially
compromising the safety of the vehicle. Despite research in this field, having
a real-world feasible online slip and skid compensation remains challenging due
to the complexity of wheel-terrain interaction in outdoor environments. This
paper proposes a novel trajectory tracking technique featuring real-world
feasible online slip and skid compensation at the vehicle level for
skid-steering mobile robots operating outdoors. The approach employs
sliding-mode control to design a robust trajectory tracking system, accounting
for the inherent uncertainties in this type of robot. To estimate the robot's
slipping and undesired skidding and compensate for them in real-time, two
previously developed deep learning models are integrated into the
control-feedback loop. The main advantages of the proposed technique are that
it (1) considers two slip-related parameters for the entire robot, as opposed
to the conventional approach involving two slip components for each wheel along
with the robot's skidding, and (2) has an online real-world feasible slip and
skid compensator, reducing the tracking errors in unforeseen environments.
Experimental results demonstrate a significant improvement, enhancing the
trajectory tracking system's performance by over 27%.
