Safe Robotic Motion Planning

Safe robotic motion planning focuses on developing algorithms that enable robots to navigate complex environments while guaranteeing collision avoidance and system stability. Current research heavily utilizes model predictive control (MPC) often incorporating control barrier functions (CBFs) or Lyapunov functions as safety constraints, sometimes learned from human feedback or refined by diffusion models. These methods aim to generate safe and efficient trajectories, even in dynamic, uncertain environments, addressing challenges like infeasibility in crowded scenarios. This field is crucial for deploying robots safely in real-world applications, improving the reliability and trustworthiness of autonomous systems.