Steering with Contingencies: Combinatorial Stabilization and Reach-Avoid Filters
Provides a practical solution for safety-critical autonomous systems (e.g., landing, navigation) that need contingency plans, with computational efficiency for real-time operation.
This work formalizes the problem of steering toward a target while retaining the ability to divert to at least r out of p alternative sites, and develops tractable control filters using combinatorial constraints. The filters require only p+1 constraints, preventing combinatorial blow-up and enabling real-time safe switching.
In applications such as autonomous landing and navigation, it is often desirable to steer toward a target while retaining the ability to divert to at least $r$ (out of $p$) alternative sites if conditions change. In this work, we formalize this combinatorial contingency requirement and develop tractable control filters for enforcement. Combinatorial stabilization requires asymptotic stability of a selected equilibrium while ensuring the trajectory remains within the safe region of attraction of at least $r$-out-of-$p$ candidates. To enforce this requirement, we use control Lyapunov functions (CLFs) to construct regions of attraction, which are combined combinatorially within an optimization-based filter. Combinatorial targeting extends this framework to finite-horizon problems using Hamilton-Jacobi backward reach-avoid sets, accommodating shrinking reachable regions due to finite horizons or resource depletion. In both formulations, the resulting combinatorial stability filter and combinatorial reach-avoid filter require only $p+1$ constraints, preventing combinatorial blow-up and enabling safe real-time switching between targets. The framework is demonstrated on two examples where the filters ensure steering with contingency and enable safe diversion.