Trajectory Generation

Trajectory generation focuses on creating optimal paths for robots or agents, considering factors like safety, efficiency, and task completion. Current research emphasizes diverse approaches, including optimization-based methods, generative models (like diffusion models and transformers), and reinforcement learning, often incorporating constraints from sensor data (e.g., point clouds, images) and environmental dynamics. These advancements are crucial for improving autonomous navigation in complex environments, enabling applications such as autonomous driving, multi-robot coordination, and wildlife tracking. The development of more efficient and robust trajectory generation techniques is driving progress across various fields.