Walking with Confidence: Safety Regulation for Full Order Biped Models
This provides safety guarantees for complex walking robot models, addressing a critical issue in robotics to prevent falls, though it is incremental by extending existing methods to higher dimensions.
The paper tackles the problem of ensuring safety in walking robots by combining sums-of-squares optimization with hybrid zero dynamics to generate a guaranteed safe set for a 10-dimensional model, applied to a bipedal Rabbit model.
Safety guarantees are valuable in the control of walking robots, as falling can be both dangerous and costly. Unfortunately, set-based tools for generating safety guarantees (such as sums-of-squares optimization) are typically restricted to simplified, low-dimensional models of walking robots. For more complex models, methods based on hybrid zero dynamics can ensure the local stability of a pre-specified limit cycle, but provide limited guarantees. This paper combines the benefits of both approaches by using sums-of-squares optimization on a hybrid zero dynamics manifold to generate a guaranteed safe set for a 10-dimensional walking robot model. Along with this set, this paper describes how to generate a controller that maintains safety by modifying the manifold parameters when on the edge of the safe set. The proposed approach, which is applied to a bipedal Rabbit model, provides a roadmap for applying sums-of-squares verification techniques to high dimensional systems. This opens the door for a broad set of tools that can generate safety guarantees and regulating controllers for complex walking robot models.