Trajectory Design
Trajectory design focuses on optimizing the path of a moving entity, whether a spacecraft, robot, or unmanned aerial vehicle, to achieve specific objectives like minimizing fuel consumption, maximizing efficiency, or ensuring safety. Current research emphasizes developing computationally efficient and robust algorithms, often employing machine learning techniques such as reinforcement learning, diffusion models, and neural networks, to handle complex constraints and high-dimensional search spaces. These advancements are crucial for various applications, including autonomous navigation, robotics, and aerospace engineering, enabling more efficient and reliable operation in diverse and challenging environments.
Papers
Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance
Julia Briden, Trey Gurga, Breanna Johnson, Abhishek Cauligi, Richard Linares
Diffusion Policies for Generative Modeling of Spacecraft Trajectories
Julia Briden, Breanna Johnson, Richard Linares, Abhishek Cauligi
TraSCE: Trajectory Steering for Concept Erasure
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya, Yuhta Takida, Nasir Memon, Julian Togelius, Yuki Mitsufuji
When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation
Xuhui Zhang, Wenchao Liu, Jinke Ren, Huijun Xing, Gui Gui, Yanyan Shen, Shuguang Cui