aiqbench analysis
This arXiv preprint describes a fully onboard pipeline that uses a 0.5B-parameter LLM, with retrieval grounding and schema-constrained output, to assign per-category risk to obstacles for drone navigation. The reported gains (about 3x fewer collisions in simulation, and landing success rising from 34% to 96%) come from Gazebo tests, not real-world flights, and the authors note limits from the 224-pixel encoder on small or distant objects. For AI readers, the useful takeaway is the design pattern: caching LLM verdicts per object class to keep inference off the control-critical path on edge hardware.
Onboard Lightweight LLM Reasoning for Human-Aware Safe UAV Navigation
arXiv:2610.22092v1 Announce Type: new Abstract: Aerial robots operating near people must treat a pedestrian, a parked car, and a wall differently, yet conventional perception--planning stacks reduce all of them to occupied geometry---a \emph{human-aware safety gap} in which category-specific clearance is unavailable to the planner and temporary occlusions remove pedestrian-specific constraints from its map. We present a modular, fully onboard pipeline that closes this gap by turning visual observations into context-dependent safety constraints for real-time UAV navigation. A shared BLIP encoder drives scene captioning and text-conditioned segmentation, which is fused with depth to localize obstacles in 3D. Obstacle-specific risk is inferred by a lightweight LLM (Qwen-2.5-0.5B) stabilized with retrieval-augmented generation and grammar-constrained decoding that restricts its output to a planner-compatible JSON schema. The LLM reasons once per object category rather than per frame: its verdicts are cached per class, so previously seen categories are resolved without inference and new ones trigger a fresh retrieval-grounded query; detections are additionally given persistent identities and held in the map across occlusions. The perception pipeline runs at 10\,Hz on a Jetson Orin NX, while fallback LLM queries take approximately 0.8\,s and remain off the control-critical path. In Gazebo, low-altitude urban navigation among pedestrians and vehicles shows a roughly $3\times$ reduction in collision events over a geometric planner, and semantics-aware landing among moving actors raises success from 34\% to 96\%. The 224-pixel encoder input limits reliable small-object perception at range; conservative pedestrian margins and short-term persistence mitigate this limitation during navigation.
Abstract:Aerial robots operating near people must treat a pedestrian, a parked car, and a wall differently, yet conventional perception--planning stacks reduce all of them to occupied geometry---a \emph{human-aware safety gap} in which category-specific clearance is unavailable to the planner and temporary occlusions remove pedestrian-specific constraints from its map. We present a modular, fully onboard pipeline that closes this gap by turning visual observations into context-dependent safety constraints for real-time UAV navigation. A shared BLIP encoder drives scene captioning and text-conditioned segmentation, which is fused with depth to localize obstacles in 3D. Obstacle-specific risk is inferred by a lightweight LLM (Qwen-2.5-0.5B) stabilized with retrieval-augmented generation and grammar-constrained decoding that restricts its output to a planner-compatible JSON schema. The LLM reasons once per object category rather than per frame: its verdicts are cached per class, so previously seen categories are resolved without inference and new ones trigger a fresh retrieval-grounded query; detections are additionally given persistent identities and held in the map across occlusions. The perception pipeline runs at 10\,Hz on a Jetson Orin NX, while fallback LLM queries take approximately 0.8\,s and remain off the control-critical path. In Gazebo, low-altitude urban navigation among pedestrians and vehicles shows a roughly $3\times$ reduction in collision events over a geometric planner, and semantics-aware landing among moving actors raises success from 34\% to 96\%. The 224-pixel encoder input limits reliable small-object perception at range; conservative pedestrian margins and short-term persistence mitigate this limitation during navigation.
Robotics (cs.RO)
arXiv:2610.22092 [cs.RO]
(or arXiv:2610.22092v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.22092 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 333 words
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