loading page

Model predictive path-following framework for generalised N-trailer vehicles in the presence of dynamic obstacles modelled as soft constraints
  • Nestor Nahuel Deniz ,
  • Franco Jorquera,
  • Fernando Auat Cheein
Nestor Nahuel Deniz
Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria

Corresponding Author:[email protected]

Author Profile
Franco Jorquera
Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria
Fernando Auat Cheein
Heriot-Watt University, Edinburgh Centre for Robotics, School of Engineering and Physical Sciences, National Robotarium, Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria

Abstract

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.
06 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv