Steering with Contingencies: Combinatorial Stabilization and Reach-Avoid Filters
A new framework prevents combinatorial explosion, requiring only p+1 constraints for real-time safe switching between targets.
A team from Caltech and UC San Diego, led by Yana Lishkova and Aaron D. Ames, has published a groundbreaking paper titled 'Steering with Contingencies: Combinatorial Stabilization and Reach-Avoid Filters.' The work addresses a critical challenge in robotics and autonomous systems: how to guarantee a system can reach a primary goal while maintaining the ability to safely divert to multiple alternative targets if conditions change. This 'combinatorial contingency' requirement is formalized for the first time, with applications ranging from drone navigation to autonomous vehicle landing, where a craft must be able to abort to one of several safe landing zones.
The core of the framework is two novel 'control filters.' The Combinatorial Stability Filter uses control Lyapunov functions (CLFs) to construct safe regions of attraction around each potential target. The Combinatorial Reach-Avoid Filter extends this to finite-horizon problems using Hamilton-Jacobi backward reachability analysis. The major technical breakthrough is computational tractability. Instead of the problem's complexity exploding combinatorially with the number of backup sites (p), the new method requires only p+1 constraints to be solved in real-time. This enables safe, online switching between targets without overwhelming the system's processor.
The research demonstrates the framework on two examples, proving it can enforce steering with contingency and enable safe diversion. This represents a significant step beyond traditional control methods that typically plan for a single objective, making autonomous systems far more robust and adaptable to dynamic, uncertain environments where backup plans are essential for safety.
- Formalizes 'combinatorial contingency' where a system must reach a primary target while staying within safe regions of at least r-out-of-p backups.
- Introduces two tractable control filters that prevent combinatorial explosion, requiring only p+1 constraints for real-time operation.
- Demonstrated on autonomous systems applications, enabling safe diversion and robust planning for dynamic scenarios like landing.
Why It Matters
Enables more reliable and safer autonomous robots and vehicles that can dynamically adapt to failures or changing conditions with guaranteed safety.