Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation

arXiv:2610.09302v1 Announce Type: new Abstract: Safe navigation in dynamic environments requires robots to plan under obstacle predictions whose errors are uncertain, non-stationary, and can induce rare but safety-critical failures. Existing control-barrier-function safety filters can reject immediately unsafe controls, but they provide little guidance on when the current finite-horizon planning mode itself is becoming unsafe as prediction uncertainty evolves. We propose Conformal Event-Triggered Risk-Certified Replanning (CERT-Replan), a framework that uses calibrated barrier risk as an early-warning signal for replanning. CERT-Replan calibrates horizon-indexed obstacle-prediction residuals online and uses the resulting uncertainty radii to evaluate dynamic-obstacle safety margins. A one-step safety filter protects the next applied control, while a horizon-level risk monitor evaluates the upper-tail CVaR of predicted barrier-violation losses along the current MPC rollout. When this risk exceeds an allocated budget, CERT-Replan rejects the current planning mode and selects a lower-risk alternative, such as a different speed profile, corridor, or homotopy class, rather than repeatedly correcting the same nominal plan. In a non-stationary benchmark, CERT-Replan achieves an (83.3%) collision reduction relative to the safety-filter-only baseline extcolor{black}{and a (77.8%) reduction relative to simple replanning triggers}, while reducing average safety-filter intervention by (32.4%). extcolor{black}{With Trajectron++, CERT-Replan achieves (96%) collision-free operation. Hardware experiments and onboard runtime profiling demonstrate computational feasibility.}

Source: arXiv cs.RO | 2026-10-08