A sub-optimal sampling based method for path planning

TL;DR

This paper introduces a sampling-based search algorithm for path planning of non-holonomic systems, utilizing a bicycle model, stability analysis, and optimal control to efficiently find sub-optimal paths.

Contribution

The paper presents a novel sampling-based path planning method that combines stability analysis and optimal control for non-holonomic systems, improving efficiency.

Findings

Successful path planning in various scenarios

Reduced computation time with proper integration methods

Effective connection to destination using optimal control

Abstract

In this paper a search algorithm is proposed to find a sub optimal path for a non-holonomic system. For this purpose the algorithm starts sampling the front part of the vehicle and moves towards the destination with a cost function. The bicycle model is used to define the non-holonomic system and a stability analysis with different integration methods is performed on the dynamics of the system. A proper integration method is chosen with a reasonably large step size in order to decrease the computation time. When the tree is close enough to destination the algorithm returns the path and in order to connect the tree to destination point an optimal control problem using single shooting method is defined. To test the algorithm different scenarios are tested and the simulation results show the success of the algorithm.