Effective obstacle detection and avoidance play pivotal roles in the
implementation of autonomous navigation systems. While numerous authors
have addressed obstacle avoidance for single unicycles and car-like
vehicles, this work extends the scope to encompass generalised N-trailer
vehicles, consisting of a single active segment pulling an arbitrary
number of trailers. In contrast to treating obstacles as hard
constraints or barrier functions, we introduce a unique approach by
modelling them as soft constraints. Gaussian functions are seamlessly
integrated into the objective function of the model predictive
controller, preserving the convexity of the search space and
significantly alleviating computational demands. Although this strategy
allows regions occupied by obstacles to remain viable for navigation, we
counteract this by thoughtfully designing the amplitude of the Gaussian
function. This design is influenced by various components within the
formulation, discouraging navigation through obstacle-occupied spaces.
The effectiveness of this approach is substantiated through a series of
simulated and field experiments involving a tractor pulling two
trailers. These experiments showcase the method’s proficiency in
navigating around obstacles while maintaining computational efficiency,
thereby affirming its practical viability in real-world scenarios.