AeroThrow: An Autonomous Aerial Throwing System for Precise Payload Delivery

2025-07-18

AI Summary

To address low delivery accuracy in drone airdrops caused by abrupt control-mode transitions, system latency, and trajectory tracking errors, this paper proposes an autonomous aerial delivery system based on an active aerial robotic manipulator. The method innovatively integrates nonlinear model predictive control (NMPC) with a hierarchical disturbance compensation framework. By leveraging the manipulator’s active degrees of freedom, it enables online impact-point trajectory optimization and real-time compensation of tracking errors. Additionally, a smooth, continuous impact-point constraint is introduced to reduce sensitivity to release timing. Simulation and hardware experiments demonstrate that the proposed approach significantly enhances system robustness, response agility, and delivery accuracy—reducing positional error by approximately 62%. This work establishes an engineering-feasible paradigm for high-precision airdrop operations in complex, dynamic environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Autonomous aerial systems play an increasingly vital role in a wide range of applications, particularly for transport and delivery tasks in complex environments. In airdrop missions, these platforms face the dual challenges of abrupt control mode switching and inherent system delays along with control errors. To address these issues, this paper presents an autonomous airdrop system based on an aerial manipulator (AM). The introduction of additional actuated degrees of freedom enables active compensation for UAV tracking errors. By imposing smooth and continuous constraints on the parabolic landing point, the proposed approach generates aerial throwing trajectories that are less sensitive to the timing of payload release. A hierarchical disturbance compensation strategy is incorporated into the Nonlinear Model Predictive Control (NMPC) framework to mitigate the effects of sudden changes in system parameters, while the predictive capabilities of NMPC are further exploited to improve the precision of aerial throwing. Both simulation and real-world experimental results demonstrate that the proposed system achieves greater agility and precision in airdrop missions.

Problem

• Addresses abrupt control switching and system delays in airdrops
• Compensates UAV tracking errors via aerial manipulator actuation
• Improves airdrop precision using NMPC and disturbance compensation

Innovation

• Uses aerial manipulator for active error compensation
• Imposes smooth constraints on parabolic landing
• Incorporates NMPC for disturbance compensation