An innovative integrated path planning and trajectory tracking framework for a quadrotor slung load system in an urban environment

Abstract

In this paper, a hybrid path planning and trajectory tracking controller for a quadrotor slung load system is presented. This controller has benefited from the fuzzy inference system in its tracking and swing stabilization tasks. This system also plays an important role in creating a grid-based obstacle avoidance method that works based on the concept of generating Non-stationary Artificial Potential Field (NAPF) in the proximity of the objects tracking a virtual target on the path computed by A* algorithm. The weakness of the conventional artificial potential field method, that is to stuck in local minima, is now resolved by this so-called hybrid A*-NAPF method in which the potential field is non-stationary. The A* algorithm as a fast optimal path determination method computes the discrete initial path using a network of grids already generated through transferring the environment to a binary occupancy map. The performance of the whole integrated system is investigated through various simulation scenarios including those with moving obstacles. The results indicate a very good performance with high adaptability characteristic.