SCOPE: Field-of-View-Aware Path Planning in Unknown 3D Environments via Safety-Volume Certification

2026-08-05

AI Summary

This work addresses the challenge of safe navigation for robots with limited field-of-view in unknown 3D environments by proposing the SCOPE framework, which introduces, for the first time, a safety volume certification mechanism to achieve conditionally complete planning. The approach decouples path planning from certification execution through an online-constructed certification graph: uncertified waypoints are treated as observation tasks, and safe, smooth trajectories are generated by integrating goal-centric viewpoint search with perception-aware trajectory optimization. The framework further incorporates a novel certification preview mechanism and a recursive resolution strategy for observation obligations, significantly enhancing both navigation efficiency and safety. Experimental results demonstrate 100% success across 60 random tasks with near-zero intrusion into uncertified inflated regions; the certification preview reduces average task completion time by 27% and is validated on a real robotic platform.

Abstract

Safe navigation with a body-mounted limited-field-of-view sensor requires the complete robot-inflated volume of an intended motion to be observed and verified free before execution. We formulate this requirement as online safety-volume certification in an unknown voxel map and construct a certified graph whose vertices correspond exactly to positions with fully known-free safety volumes. Based on this representation, we propose SCOPE (Safety Certification through Observation Planning and Execution), a planning framework that decouples optimistic goal-directed guidance from certified execution. SCOPE converts the first uncertified point along an optimistic route into an explicit observation obligation, resolves it through target-centric viewpoint search, and recursively clears intermediate obligations when useful viewpoints are not yet certified-reachable. A certified preview mechanism and an observation-aware trajectory optimization backend enable smooth execution. We prove conditional complete planning: under ideal monotone sensing and exhaustive finite-domain graph search, SCOPE reaches the goal whenever a finite feasible sequence of certified sensing actions exists within its planning primitives. Across 60 randomized tasks in three unknown 3D environments, SCOPE reaches every goal while maintaining near-zero entry into non-certified inflated space. Preview reduces mean mission time by 27%, and real-robot demonstrations in two representative scenarios validate the complete system.

Problem

• field-of-view
• safety-volume certification
• unknown 3D environments
• safe navigation
• limited-field-of-view sensor

Innovation

• safety-volume certification
• field-of-view-aware planning
• certified graph
• observation planning
• conditional completeness