AutoTrans: 自主无人机吊载系统规划与控制 IEEE RAL
机器人社区对自主空中运输越来越感兴趣。 无人机吊载运输系统与其它系统相比具有机械结构简单、灵活等优势，但对规划和控制算法提出了很大挑战。 为了实现完全自主的空中运输，我们提出了一个系统性的解决方案来解决这些困难。首先，提出了一种实时规划方法，考虑系统的时变形状和非线性动力学来生成平滑轨迹，从而确保全身安全性和动力学可行性。 此外，设计了一种具有分层干扰补偿策略的自适应 NMPC（非线性模型预测控制），旨在克服未知的外部扰动和不准确的模型参数带来的影响。大量实验表明，即使在十分狭窄受限的环境中，我们的方法也能够在线生成高质量的轨迹，并且即使在存在较大不外部不确定性的情况下也能准确跟踪高速飞行轨迹。我们计划开源我们的代码给予社区。
The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community.
南方科技大学助理教授，博导；微分智飞联合创始人；TRO Best Paper一作；研究机器人/具身； robotics-star.com/
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