Time-Optimal Trajectory Planning With Clearly Defined Initial Guess for Aerial Suspended Payload Throwing
Rui Feng Cao, Yongchun Fang, Xiao Liang · IEEE Transactions on Automation Science and Engineering · 2025
Autonomous Aerial Vehicles (AAVs), particularly quadrotors, have gained substantial attention in recent years due to their high agility, substantial convenience, and significant potential in hazardous missions such as military surveillance and disaster relief. This paper focuses on the aerial throwing problem, aiming to develop a time-optimal method for air-dropping cable-suspended payloads. The contributions of the paper are: 1) a fast approach is presented to streamline the quadrotor’s state management by directly mapping and planning at the quadrotor state space (position, velocity, acceleration); 2) a clearly defined initial guess is provided for aerial suspended throwing tasks, which speeds up the planning process. This methodology not only enhances the convenience of quadrotor navigation, but also fosters a more direct and efficient control scheme. The efficacy and feasibility of the proposed method are validated through both numerical simulations and practical experiments, demonstrating the potential for rapid and accurate payload throwing with cable-suspended systems. Note to Practitioners—This study is driven by the need to enhance aerial payload throwing task in hazardous scenarios, such as disaster relief and military surveillance where precision and speed are crucial. While quadrotors serve as agile platforms for such operations, existing methods lack a rapid planning approach that can directly plan at the quadrotor state space (position, velocity, acceleration). Our work introduces a warm start strategy, which significantly hastens the planning process, enabling faster throwing of cable-suspended payloads. Future extensions of this work could focus on integrating adaptive elements that respond to environmental feedback in real-time, thus broadening the practical applicability of the method in real-world conditions.