Safety Embedded Differential Dynamic Programming Using Discrete Barrier States

Certified safe control is a growing challenge in robotics, especially when performance and safety objectives must be concurrently achieved. In this work, we extend the barrier state (BaS) concept, recently proposed for stabilization of continuous time systems, to embedded trajectory optimization discrete systems using states (DBaS). The constructed DBaS into model safety-critical system integrating system's dynamics objectives. Thereby, policy directly supplied by information through state. This allows us employ with differential dynamic programming (DDP) plan execute optimal trajectories. algorithm leveraged on various planning problems including wheeled robot navigation randomized complex environments quadrotor safely perform reaching tracking tasks. DBaS-based DDP (DBaS-DDP) shown consistently outperform penalty methods commonly used approximate constrained as well CBF-based filters.