Nonlinear receding-horizon differential game for drone racing along a three-dimensional path

TL;DR

This paper introduces a game-theoretic control framework called NRHDG for high-speed drone racing along 3D paths, improving robustness and strategic maneuvering in adversarial scenarios.

Contribution

It develops a novel path-following method, a real-time maneuver switching potential function, and a performance metric for competitive drone racing control.

Findings

NRHDG outperforms NMPC in overtaking efficiency.

NRHDG enhances robustness against adversarial behaviors.

Simulation results validate improved race performance.

Abstract

Drone racing involves high-speed navigation of three-dimensional paths, posing a substantial challenge in control engineering. This study presents a game-theoretic control framework, the nonlinear receding-horizon differential game (NRHDG), designed for competitive drone racing. NRHDG enhances robustness in adversarial settings by predicting and countering an opponent's worst-case behavior in real time. It extends standard nonlinear model predictive control (NMPC), which otherwise assumes a fixed opponent model. First, we develop a novel path-following formulation based on projection point dynamics, eliminating the need for costly distance minimization. Second, we propose a potential function that allows each drone to switch between overtaking and obstructing maneuvers based on real-time race situations. Third, we establish a new performance metric to evaluate NRHDG with NMPC under race scenarios. Simulation results demonstrate that NRHDG outperforms NMPC in terms of both overtaking efficiency and obstructing capabilities.