Online Trajectory Optimization

Online trajectory optimization focuses on dynamically generating optimal paths for robots and autonomous systems in real-time, adapting to changing environments and constraints. Current research emphasizes efficient algorithms, such as model predictive control and graph search methods, often integrated with machine learning models like neural networks for perception and decision-making, to handle complex scenarios involving multi-contact locomotion, hazard avoidance, and human-robot interaction. This field is crucial for advancing robotics in diverse applications, including surgery, rehabilitation, and autonomous navigation, by enabling safer, more efficient, and adaptable systems.