UAV Trajectory Planning with Path Processing

TL;DR

This paper investigates how initial guesses affect UAV trajectory planning formulated as an optimal control problem, demonstrating that good initial guesses improve planning outcomes using pseudospectral methods.

Contribution

It introduces a method that uses Lazy Theta* for initial path estimation to enhance trajectory planning in UAVs within an optimal control framework.

Findings

Initial guesses significantly influence trajectory quality.

Using Lazy Theta* improves initial path estimation.

The approach shows promising results for future UAV planning research.

Abstract

This paper examines the influence of initial guesses on trajectory planning for Unmanned Aerial Vehicles (UAVs) formulated in terms of Optimal Control Problem (OCP). The OCP is solved numerically using the Pseudospectral collocation method. Our approach leverages a path identified through Lazy Theta* and incorporates known constraints and a model of the UAV's behavior for the initial guess. Our findings indicate that a suitable initial guess has a beneficial influence on the planned trajectory. They also suggest promising directions for future research.