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 safe stabilization of continuous time systems, to safety embedded trajectory optimization for discrete time systems using discrete barrier states (DBaS). The constructed DBaS is embedded into the discrete model of the safety-critical system integrating safety objectives into the system's dynamics and performance objectives. Thereby, the control policy is directly supplied by safety-critical information through the barrier state. This allows us to employ the DBaS with differential dynamic programming (DDP) to plan and execute safe optimal trajectories. The proposed algorithm is leveraged on various safety-critical control and planning problems including a differential wheeled robot safe navigation in randomized and complex environments and on a quadrotor to safely perform reaching and tracking tasks. The DBaS-based DDP (DBaS-DDP) is shown to consistently outperform penalty methods commonly used to approximate constrained DDP problems as well as CBF-based safety filters.
翻译:在这项工作中,我们将最近为安全稳定连续时间系统而提出的屏障状态概念扩大到使用离散屏障状态的离散时间系统的安全嵌入轨迹优化。建造的DBAS嵌入了安全临界系统的独立模型,将安全目标纳入系统的动态和性能目标。因此,控制政策通过屏障状态通过安全关键信息直接提供。这使我们能够使用带有不同动态程序(DDP)的DBAS来规划和实施安全的最佳轨迹。拟议的算法被用于各种安全临界控制和规划问题,包括在随机和复杂环境中和在磁场上使用不同的轮式机器人安全导航,以安全地到达和跟踪任务。基于DBAS-DDP(DBAS-DDP)的DBAS DDP(DDP-DDP)显示,通常用来估计受限的DDP问题以及基于CBF的安全过滤器的处罚方法始终超过常规。