Opportunistic Data Offloading for Robotic Operations in Dynamically Varying Channel Environments

Heeirthan Shanthan; Winston Hurst; Yasamin Mostofi

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arXiv · 2609.04436

Opportunistic Data Offloading for Robotic Operations in Dynamically Varying Channel Environments

Heeirthan Shanthan·Winston Hurst·Yasamin Mostofi

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Abstract

This paper studies energy-efficient operation of autonomous vehicles (AVs) in dynamic environments with moving obstacles and while communicating over mmWave channels. The obstacles induce severe attenuation of the mmWave channel resulting in a highly dynamic communication environment. In this setting, we consider the problem of jointly optimizing motion and communication energy for an AV that safely navigates among dynamic obstacles toward a designated destination while ensuring timely transmission of onboard sensing or telemetry data over mmWave channels. We then seek a real-time methodology to compute energy-efficient trajectories in a setting where dynamic obstacles induce both safety constraints and time-varying mmWave blockage, leading to tightly coupled motion-communication trade-offs. We propose a nonlinear model predictive control (NMPC) framework that enables anticipative communication and motion decision-making and energy co-optimization, augmented with a control barrier function (CBF) to ensure safety. Extensive simulation results demonstrate the effectiveness of our approach, reducing total energy consumption by up to 37.3% compared to baseline strategies. Overall, our results demonstrate that the proposed NMPC-based framework significantly enhances energy efficiency and performance of AVs under dynamic, blockage-sensitive mmWave communication constraints.

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BibTeX RIS

Heeirthan Shanthan, Winston Hurst, Yasamin Mostofi. 2026-09-03. Opportunistic Data Offloading for Robotic Operations in Dynamically Varying Channel Environments. https://arxiv.org/abs/2609.04436

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