aiqbench analysis
This research introduces the "context plane," a lightweight runtime interface enabling drone fleets to dynamically adapt their cooperative perception policies based on real-time mission constraints like bandwidth and formation geometry. By decoupling policy decisions from fixed design-time configurations, the approach achieves full-sharing accuracy while reducing data exchange to just 5-10% of traditional methods without retraining underlying models. The proposed solution demonstrates that explicit context management is essential for scalable aerial robotics, offering a practical path toward efficient, adaptive multi-agent systems.
Fleets Need a Context Plane: Rethinking Cooperative Perception for Autonomous Drones
arXiv:2609.00659v1 Announce Type: new Abstract: Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-sharing policies at design time or adapt to only one context signal. This is a poor fit for aerial fleets, whose missions, bandwidth, formation geometry, and scene coverage can change during flight. We quantify the cost of context-blind sharing on UAV3D by controlling feature exchange at evaluation time using a released DiscoNet checkpoint, without retraining. Mission-aware sharing matches full-sharing accuracy while using only 5-10% of the bytes. The best tested peer selection policy changes with the byte budget, and choosing the wrong policy loses up to 7.7 AP. Moreover, under a constrained budget, two policies with the same full-scene accuracy differ by 5.9 AP within the mission region, showing that multiple context axes must be considered jointly. We therefore propose the context plane, a bounded, structured interface for runtime context. Each drone publishes a descriptor of at most 1 KB at 10 Hz, and lightweight, replaceable policies use the fleet context to decide what each drone computes, shares, and fuses. Existing sharing schemes become fixed policies within this interface. In our ROS 2 prototype on a Jetson AGX Orin, the context plane uses approximately 0.01% of the data-plane bandwidth, and each policy decision takes 0.10 ms. These results show that an explicit context interface can support low-overhead runtime adaptation without modifying or retraining the perception model.
Abstract:Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-sharing policies at design time or adapt to only one context signal. This is a poor fit for aerial fleets, whose missions, bandwidth, formation geometry, and scene coverage can change during flight. We quantify the cost of context-blind sharing on UAV3D by controlling feature exchange at evaluation time using a released DiscoNet checkpoint, without retraining. Mission-aware sharing matches full-sharing accuracy while using only 5-10% of the bytes. The best tested peer selection policy changes with the byte budget, and choosing the wrong policy loses up to 7.7 AP. Moreover, under a constrained budget, two policies with the same full-scene accuracy differ by 5.9 AP within the mission region, showing that multiple context axes must be considered jointly. We therefore propose the context plane, a bounded, structured interface for runtime context. Each drone publishes a descriptor of at most 1 KB at 10 Hz, and lightweight, replaceable policies use the fleet context to decide what each drone computes, shares, and fuses. Existing sharing schemes become fixed policies within this interface. In our ROS 2 prototype on a Jetson AGX Orin, the context plane uses approximately 0.01% of the data-plane bandwidth, and each policy decision takes 0.10 ms. These results show that an explicit context interface can support low-overhead runtime adaptation without modifying or retraining the perception model.
Robotics (cs.RO); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)
arXiv:2609.00659 [cs.RO]
(or arXiv:2609.00659v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.00659 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 336 words
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