Optimal Path Planning for Aerial Load Transportation in Complex Environments using PSO-Improved Artificial Potential Fields

TL;DR

This paper presents a novel path planning approach for aerial load transportation using an improved artificial potential field combined with Particle Swarm Optimization, optimizing trajectories in complex environments for quadrotors with payloads.

Contribution

It introduces a hierarchical control system integrating PSO and artificial potential fields for efficient obstacle avoidance and path optimization in aerial payload transportation.

Findings

Successfully navigated complex obstacle environments

Minimized transportation time and trajectory length

Enhanced payload transportation stability

Abstract

In this article, we investigate the optimal path planning for aerial load transportation in complex, dynamic, and static environments using Particle Swarm Optimization (PSO). A hierarchical optimal control system is designed for a quadrotor equipped with a cable-suspended payload, employing Euler-Lagrange equations of motion. To navigate through obstacles, an improved artificial potential field combined with the PSO algorithm is used to determine the shortest path for a virtual point, acting as a leader. This leader guides the system toward the target point while avoiding collisions with both fixed and moving obstacles. The gravitational and repulsion coefficient forces using various PSO methods are fine-tuned to achieve the best trajectory and minimize time duration. The identified point serves as the desired location for quadrotor position control, based on a sliding mode strategy. Finally, we present numerical results to demonstrate the successful transportation of the payload by the system.