A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques

LUCAS GENZELIS; MARINA MURILLO; LEONARDO GIOVANINI; GUIDO SÁNCHEZ

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS

SPRINGER

Año: 2017 vol. 91 p. 445 - 457

0921-0296

In this article we present a real-time path-planning algorithm that can be used to generate optimal and feasible paths for any kind of unmanned vehicle (UV). The proposed algorithm is based on the use of a simplified particle vehicle (PV) model, which includes the basic dynamics and constraints of the UV, and an iterated non-linear model predictive control (NMPC) technique that computes the optimal velocity vector (extcolor{blue}{magnitude} and orientation angles) that allows the PV to move towards desired targets. The computed paths are guaranteed to be feasible for any UV because: i) the PV is configured with similar characteristics (dynamics and physical constraints) as the UV, and ii) the feasibility of the optimization problem is guaranteed by the use of the iterated NMPC algorithm. As  demonstration of the capabilities of the proposed path-planning algorithm, we explore several simulation examples in different scenarios. We consider the existence of static and dynamic obstacles and a follower condition.