FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation

2026-06-16

AI Summary

This work addresses the challenge of safe and efficient trajectory planning for unmanned aerial vehicles (UAVs) in unknown, cluttered 3D environments, where limited field-of-view (FOV) and sensing range of onboard sensors hinder reliable navigation. The authors propose a novel approach that directly embeds active perception into trajectory optimization. By leveraging the UAV’s dynamics model, the method accurately encodes FOV geometric constraints in the sensor frame and introduces a velocity-triggered perception mechanism to balance exploration and motion efficiency. It employs parameterized, time-shifted active perception sub-trajectories, enabling online sensing during arbitrary 3D maneuvers without requiring prior maps or dedicated path generators. Built upon a differentiable optimization framework, the planner operates with only a coarse global path as guidance. Extensive simulations and real-world experiments demonstrate the approach’s robustness, safety, and computational efficiency across diverse unknown environments and sensor configurations.

Abstract

Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.

Problem

• field-of-view (FOV)
• active perception
• 3D navigation
• trajectory planning
• UAV

Innovation

• active perception
• FOV-constrained planning
• differentiable optimization
• UAV navigation
• 3D trajectory planning