Reliable and efficient trajectory optimization methods are a fundamental need for autonomous dynamical systems, effectively enabling applications including rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within such safety-critical application areas, the complexity of the emerging trajectory optimization problems has motivated the application of AI-based techniques to enhance the performance of traditional approaches. However, current AI-based methods either attempt to fully replace traditional control algorithms, thus lacking constraint satisfaction guarantees and incurring in expensive simulation, or aim to solely imitate the behavior of traditional methods via supervised learning. To address these limitations, this paper proposes the Autonomous Rendezvous Transformer (ART) and assesses the capability of modern generative models to solve complex trajectory optimization problems, both from a forecasting and control standpoint. Specifically, this work assesses the capabilities of Transformers to (i) learn near-optimal policies from previously collected data, and (ii) warm-start a sequential optimizer for the solution of non-convex optimal control problems, thus guaranteeing hard constraint satisfaction. From a forecasting perspective, results highlight how ART outperforms other learning-based architectures at predicting known fuel-optimal trajectories. From a control perspective, empirical analyses show how policies learned through Transformers are able to generate near-optimal warm-starts, achieving trajectories that are (i) more fuel-efficient, (ii) obtained in fewer sequential optimizer iterations, and (iii) computed with an overall runtime comparable to benchmarks based on convex optimization.

翻译：可靠高效的轨迹优化方法是自主动态系统的基本需求，有效支持火箭着陆、高超声速再入、航天器交会对接等应用。在此类安全关键应用领域中，新兴轨迹优化问题的复杂性促使人们应用人工智能技术来提升传统方法的性能。然而，当前基于人工智能的方法要么试图完全取代传统控制算法，从而缺乏约束满足性保证并产生高昂的仿真成本，要么仅通过监督学习模仿传统方法的行为。为解决这些局限性，本文提出了自主交会Transformer（ART），并从预测与控制两个角度评估了现代生成模型求解复杂轨迹优化问题的能力。具体而言，本研究评估了Transformer在以下两方面能力：（i）从先前收集的数据中学习近似最优策略；（ii）为求解非凸最优控制问题的序列优化器提供热启动，从而保证硬约束满足性。从预测角度看，结果表明ART在预测已知燃料最优轨迹方面优于其他基于学习的架构。从控制角度看，实证分析显示，通过Transformer学得的策略能够生成近似最优的热启动，实现的轨迹（i）更省燃料，（ii）在更少的序列优化迭代次数内获得，（iii）总运行时间与基于凸优化的基准方法相当。