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Embodied AI-empowered Low Altitude Economy: Integrated Sensing, Communications, Computation, and Control (ISC3)

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arxiv 2412.19996 v1 pith:PH6TW5GG submitted 2024-12-28 cs.NI eess.SP

Yaoqi Yang , Yong Chen , Jiacheng Wang , Geng Sun , Dusit Niyato This is my paper · ORCID
classification cs.NIeess.SP
keywords isc3controleai-enabledresearchaltitudecasechallengescommunications
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Low altitude economy (LAE) holds immense potential to drive urban development across various sectors. However, LAE also faces challenges in data collection and processing efficiency, flight control precision, and network performance. The challenges could be solved by realizing an integration of sensing, communications, computation, and control (ISC3) for LAE. In this regard, embodied artificial intelligence (EAI), with its unique perception, planning, and decision-making capabilities, offers a promising solution to realize ISC3. Specifically, this paper investigates an application of EAI into ISC3 to support LAE, exploring potential research focuses, solutions, and case study. We begin by outlining rationales and benefits of introducing EAI into LAE, followed by reviewing research directions and solutions for EAI in ISC3. We then propose a framework of an EAI-enabled ISC3 for LAE. The framework's effectiveness is evaluated through a case study of express delivery utilizing an EAI-enabled UAV. Finally, we discuss several future research directions for advancing EAI-enabled LAE.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

    cs.LG 2025-10 conditional novelty 5.0 of 10

    A hierarchical ARPO+LLaRA framework that jointly sets image resolution, transmit power, and UAV trajectory reduces simulated latency for onboard VLM inference in low-altitude economy networks.

  2. Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

    eess.SP 2025-05 conditional novelty 4.0 of 10

    A framework for deploying large AI models across ground, aerial, and cloud layers for low-altitude economy services, with two illustrative real-world case studies.

  3. Hierarchical Low-Altitude Wireless Network Empowered Air Traffic Management

    cs.NI 2025-09 conditional novelty 3.0 of 10

    Presents a hierarchical low-altitude wireless network (HLWN) for air traffic management, with grid corridors, multi-modal monitoring, and tiered collision avoidance, validated only by a simple simulation.

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