Learning Based Trajectory Planning

Learning-based trajectory planning focuses on using artificial intelligence to generate optimal paths for robots and autonomous vehicles, aiming for efficiency, safety, and adaptability in complex environments. Current research heavily utilizes deep reinforcement learning and neural networks, often combined with model-based optimization techniques to improve training stability, handle uncertainties, and achieve real-time performance. This approach is significantly impacting fields like autonomous driving, drone navigation, and IoT networks by enabling more robust, efficient, and human-like autonomous systems, particularly in dynamic and unpredictable scenarios.