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Edge Computing and its Application in Robotics: A Survey

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arxiv 2507.00523 v1 pith:Q2WDLFKB submitted 2025-07-01 cs.RO cs.DCcs.NI

Edge Computing and its Application in Robotics: A Survey

Nazish Tahir , Ramviyas Parasuraman This is my paper
classification cs.RO cs.DCcs.NI
keywords edgeroboticscomputingrecentapplicationschallengesdomainintelligence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The Edge computing paradigm has gained prominence in both academic and industry circles in recent years. By implementing edge computing facilities and services in robotics, it becomes a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network's edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics.

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Cited by 4 Pith papers

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

  1. HeteroMosaic: Exposing and Exploiting Heterogeneous Execution Opportunities for Energy-Efficient Edge LLM Inference

    cs.DC 2026-07 conditional novelty 7.0

    HeteroMosaic uses micro-batching and trace-guided co-optimization to split edge LLM prefill across iGPU and NPU, achieving up to 1.73-2.05x speedups and 45.3% energy reduction on AMD Ryzen AI.

  2. TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs

    cs.DC 2026-06 unverdicted novelty 6.0

    TileFuse introduces fused kernels and data layouts for W4A16/W8A16 on AMD XDNA2 NPUs, reporting up to 2.0x lower LLM prefilling latency and 64.6% lower energy versus baselines.

  3. HeteroMosaic: Exposing and Exploiting Heterogeneous Execution Opportunities for Energy-Efficient Edge LLM Inference

    cs.DC 2026-07 conditional novelty 5.0

    HeteroMosaic co-schedules edge LLM inference across iGPU and NPU via roofline analysis and micro-batches, claiming up to ~2× speedup and ~45% energy reduction on AMD Ryzen AI SoCs.

  4. TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs

    cs.DC 2026-06 unverdicted novelty 4.0

    TileFuse introduces a fused kernel library enabling AWQ W4A16/W8A16 quantized LLM inference on AMD NPUs, reporting up to 2.0x lower prefilling latency and 64.6% lower energy on Ryzen AI laptops.