Original title: Opportunistic Data Offloading for Robotic Operations in Dynamically Varying Channel Environments
Heeirthan Shanthan, Winston Hurst, Yasamin Mostofi · September 2026
Autonomous vehicles need energy to drive and to send sensor data over high-frequency millimeter-wave wireless signals. Moving obstacles can easily block these signals, causing severe signal loss and wasting energy. Researchers wanted to help vehicles reach their destinations safely while cutting down power use. They designed a smart planning system that looks ahead to make driving and data-sharing decisions together. It pairs a predictive control method with safety barriers to dodge moving obstacles and avoid blocked signals. This allows the vehicle to send data only when the connection is clear and move efficiently. In extensive simulations, this method cut total energy use by up to 37.3% compared to baseline strategies, keeping vehicles safe while keeping communication smooth.
You are hiking and need to send a picture, but trees block the signal. You must stop in clearings to transmit, while also avoiding dangerous rocks. Balancing movement and data sending is tricky. Here, an autonomous vehicle does the same: it plans its path and data transfers together to save energy and stay safe.
Autonomous vehicles like delivery drones and self-driving cars need to send data while navigating. Moving obstacles often block the radio signal, forcing inefficient stop-and-go behavior. This research shows that by planning movement and data transmission together, the vehicle can save over a third of its energy, extending battery life and enabling longer missions.
The abstract reports only simulation results; real-world performance may differ. The method assumes specific models for obstacle motion and mmWave propagation that may not capture all real-world complexities. Generalization to other types of obstacles, communication systems, or multi-vehicle scenarios is not established.
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Opportunistic Data Offloading for Robotic Operations in Dynamically Varying Channel Environments — Heeirthan Shanthan, Winston Hurst, Yasamin Mostofi
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.