StochSIPP: Safe Interval Path Planning in Stochastic Dynamic Environments

2026-08-01

AI Summary

This work addresses safe navigation in dynamic environments with uncertain, time-varying obstacles by anticipating local observations. It introduces the first integration of precise contingency planning with Safe Interval Path Planning (SIPP) to generate formally verified safe macro-actions. The approach performs bounded AND/OR search over a cached action–observation graph to select optimal action sequences for each reachable observation. To guide search efficiently, it employs optimistic and robust SIPP relaxations that yield admissible heuristic bounds. Decisions are made dynamically based on local observations, enabling real-time adaptation. Experiments demonstrate superior performance over fixed-path baselines in controlled road networks and successful planning in gated scenarios where conservative methods fail. The study also reveals a scalability bottleneck as observation uncertainty increases.

Abstract

Safe navigation under uncertain time-dependent blockage requires anticipating observations before committing to motion. We present StochSIPP, an exact contingent planner for temporal roadmaps with uncertain edge and vertex statuses revealed locally during execution. StochSIPP uses SIPP to generate certified-safe macro-actions that terminate at the next observation or the goal, and bounded AND/OR search over a cached action--observation graph to select actions for every reachable observation outcome. Optimistic and robust SIPP relaxations provide admissible lower and upper bounds for bounded AND/OR search. When every interval declared deterministically safe is truly safe, sensing is exact, and execution follows the planned timing, the resulting policy is provably collision-free. With correct independent probabilities and complete action and outcome generation, it minimizes expected arrival time within the roadmap and horizon. Experiments on controlled roadmap instances show that StochSIPP preserves the observed success of safe fixed-path baselines while reducing arrival time, and solves gated scenarios in which conservative fixed-path planners return no plan. A scalability study further reveals rapid growth as the number of simultaneously observed uncertain statuses increases.

Problem

• stochastic environments
• safe path planning
• dynamic obstacles
• contingent planning
• temporal roadmaps

Innovation

• Stochastic planning
• Safe Interval Path Planning
• Contingent planning
• AND/OR search
• Temporal roadmaps