Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation

Richie R. Suganda; Bin Hu

cs.ROOct 7, 2026

Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation

Authors: Richie R. Suganda, Bin Hu

Organizations: Department of Electrical Engineering and Computer University of Houston Houston, TX 77004, USA · Department of Engineering Technology University of Houston Houston, TX 77004, USA

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 (\emph{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 \textcolor{black}{and a $77.8\%$ reduction relative to simple replanning triggers}, while reducing average safety-filter intervention by $32.4\%$ . \textcolor{black}{With Trajectron++, CERT-Replan achieves $96\%$ collision-free operation. Hardware experiments and onboard runtime profiling demonstrate computational feasibility.}

Figures & tables

Figure 1: CERT-Replan framework. The CERT layer monitors the horizon risk of the current local planning mode and triggers replanning when the mode is no longer risk-certified. The AAC-DPCBF filter enforces one-step tail-risk-aware safety under calibrated prediction uncertainty.

Figure 2: Simulation trajectories. (a) Crowded dynamic environment with stationary Gaussian uncertainty. Nominal DPCBF and TEB collide, while AAC-DPCBF and CERT-Replan remain collision-free. Replanning gives modest gains because alternatives have similar risk. (b) Nonstationary corridor with spatially varying uncertainty. The inset shows prediction error shifting from Gaussian to bounded heavy-tailed uncertainty, prompting CERT-Replan to trigger a lower-risk mode change.

Method	Success( $\%$ )	Collision( $\%$ )	Path length( $m$ )	$\\|u-\bar{u}_{k}\\|_{2}$	Replans
DPCBF [ 29 ]	19	81	47.6 $\pm 0.7$	0.092 $\pm 0.057$	-
TEB [ 32 ]	85	15	47.3 $\pm 0.4$	-	-
ICP [ 20 ]	63	37	48.2 $\pm 0.8$	-	-
AAC-DPCBF (Ours)	96	4	51.1 $\pm 3.2$	0.237 $\pm 0.101$	-
CERT-Replan (Ours)	97	3	51.9 $\pm 3.2$	0.156 $\pm 0.152$	35 $\pm 3.9$

Table 1: Crowded dynamic-environment results over

100

randomized trials with

30

moving obstacles and unknown obstacle uncertainties. The proposed CERT-Replan achieves the highest success rate and lowest collision rate.

Method	Success( $\%$ )	Collision( $\%$ )	Path length( $m$ )	$\\|u-\bar{u}_{k}\\|_{2}$	Replans
DPCBF [ 29 ]	3	97	46.0 $\pm 0.1$	0.062 $\pm 0.045$	-
TEB [ 32 ]	66	34	46.2 $\pm 0.4$	-	-
ICP [ 20 ]	80	20	47.0 $\pm 0.9$	-	-
AAC-DPCBF (Ours)	79	12	55.7 $\pm 6.1$	0.327 $\pm 0.121$	-
Slack-Replan	83	9	54.1 $\pm 7.0$	$0.234\pm 0.066$	$14.2\pm 5.2$
Periodic-Replan	81	9	54.8 $\pm 5.1$	$0.298\pm 0.078$	$38.5\pm 8.9$

Table 2: Nonstationary distribution-shift results over

100

trials with

12

moving obstacles. Trigger baselines use paired obstacle realizations and random seeds. The prediction-error distribution changes from Gaussian to bounded heavy-tailed uncertainty.

Figure 3: Experiment on the Husarion ROSbot 2 Pro over $10$ trials per method. The robot executes the controller onboard, while dynamic obstacles are virtually injected for repeatability. CERT-Replan achieves the highest success rate and shorter successful-run path length than AAC-DPCBF.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Environment	Method	Success(%)	Collision(%)	Path length(m)	$\\|u-\bar{u}_{k}\\|_{2}$
Dense crowd	DPCBF [ 29 ]	19	81	47.6 $\pm 0.7$	0.092 $\pm 0.057$
	ACI+DPCBF	71	20	49.3 $\pm 2.2$	0.474 $\pm 0.327$
	Adaptive-CVaR-DPCBF	76	24	47.7 $\pm 0.9$	0.109 $\pm 0.051$
	AAC-DPCBF	96	4	51.1 $\pm 3.2$	0.237 $\pm 0.101$
Nonstationary	DPCBF [ 29 ]	3	97	46.0 $\pm 0.1$	0.062 $\pm 0.045$
	ACI+DPCBF	58	37	49.2 $\pm 2.3$	0.613 $\pm 0.335$

Appendix

Table 3: Safety-filter ablation without risk-certified replanning. All methods use the same robot dynamics, MPC horizon, DPCBF parameters, and obstacle scenarios as the main experiments.

Parameter	Value	Description
$\Delta t$	$0.1\,\mathrm{s}$	Simulation time step
$L_{\mathrm{wb}}$	$0.5\,\mathrm{m}$	Bicycle-model wheelbase
$\gamma$	$0.4$	Discrete-time DPCBF rate
$r_{\mathrm{safe}}$	$0.75\,\mathrm{m}$	Base robot-obstacle safety radius
$k_{\lambda}$	$1.0$	DPCBF curvature term
$k_{\mu}$	$1.0$	DPCBF clearance-margin term

Appendix

Table 4: Simulation parameters used across methods.

Figure 4: Real-robot experiment on the Husarion ROSbot 2 Pro. Rows compare nominal DPCBF, ACI+Adaptive-CVaR-DPCBF, and CERT-Replan across successive snapshots. The robot physically executes the onboard controller, while dynamic obstacles and prediction uncertainty are injected virtually. Nominal DPCBF collides with the dynamic obstacles, whereas CERT-Replan changes its local plan and reaches the goal.

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