Safe Trajectory

Safe trajectory planning focuses on generating paths for robots and autonomous systems that guarantee collision avoidance and adherence to safety constraints, while optimizing for efficiency and performance. Current research emphasizes integrating advanced perception (e.g., using cameras and sensor fusion) with robust planning algorithms such as Model Predictive Control (MPC), and incorporating risk assessment through methods like Control Barrier Functions (CBFs) and chance-constrained optimization. This field is crucial for enabling the safe deployment of autonomous vehicles, robots in human environments, and other safety-critical applications, driving advancements in both theoretical understanding and practical implementation.