Safe and adaptable control boosts performance in robotic systems
Context-dependent Runtime Construction of Safe Regions of Attraction from Design-Time Identification for Multi-Candidate Simplex Architectures
Robotics
Summary
Balancing safety and high performance is tricky in systems like robots that interact with the real world. The authors introduce a method that carefully switches between safe, proven controllers and faster but less certain ones, based on the current situation. This lets the system stay safe even when unexpected problems happen, while still working efficiently most of the time. They tested their approach in simulations and on real robots, showing it keeps the robot safe without slowing it down.
What this means in practice
- •For robotics engineers: Maintain safety in robots by dynamically switching between safe and high-performance controllers to handle unexpected faults without sacrificing speed.
- •For autonomous vehicle developers: Extend operating range of vehicle controllers by constructing runtime safe regions and safely switching control modes to optimize performance.
Authors
Georg Jäger, Nils-Jonathan Friedrich, Norman Seyffer, José Cecílio, António Casimiro
Abstract
Deploying safety-critical cyber-physical systems requires control strategies that reconcile high performance and adaptability with rigorous safety guarantees. Classical control strategies offer stability proofs but are conservative, while advanced optimization- and learning-based controllers achieve high performance but lack formal verification. Runtime Assurance (RTA) architectures bridge this gap by introducing their own (dis-)advantages. Safety filters use online optimization and introduce chattering, while Simplex permissiveness is bounded by the tightness of the certified Region of Attraction (RoA) of its High-Assurance (HA) controller. Targeting this conservatism, we present a two-stage RoA certification procedure leveraging contraction-based stability to establish a target set for Taylor-model-based reachability analysis. We then treat the closed-loop system under HA control as a switched system, enabling: (a) runtime construction of context-dependent safe RoA and (b) safe switching between HA candidates to optimize recovery and maximize the operating envelope of the uncertified High-Performance (HP) controller. We validate this framework on a robotic path-tracking task using a three-stage evaluation: Ordinary Differential Equation (ODE) simulation for theoretical verification, high-fidelity Gazebo simulation under unmodeled dynamics, and experiments on physical hardware. Results show that the Simplex architecture maintains safety under fault injection and successfully shields a behavioral-cloning-trained HP controller. Crucially, the decision module introduces negligible overhead (0.028 ms average, 4.13 ms worst-case), consuming under 10 % of the control period.