Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot's actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://sites.google.com/illinois.edu/comp-teleop.

Emerging farming techniques rely on smart devices such as multi-spectral cameras that collect fine-grained data, and tele-operated robots that perform tasks such as de-weeding, berry-picking, etc. These networked farm applications (requiring 10s of Mbps of throughput per device to the edge servers, with tens to hundreds of devices in a typical farm) must be supported on a wireless mesh network with limited capacity. In this work, we use these networked farm applications as a compelling case-study to design FarmNetes, a centralized traffic engineering (TE) system for wireless mesh networks. FarmNetes leverages explicit control over farm workloads to make centralized TE decisions (temporal flow schedules, sending rates, load-aware routes, and channel configurations) from an edge server, so as to best meet task requirements. FarmNetes' centralized TE decisions enable it to work with commodity devices and control how the network is shared across flows based on the desired policies (prioritization and fairness) irrespective of the underlying MAC layer link sharing mechanisms. This further enables MAC-agnostic reasoning of wireless network behavior when making TE decisions. Our evaluation, using testbeds in a farm and trace-driven simulations, shows how FarmNetes achieves 3 × higher end-end network throughput and better meets application demands, compared to status-quo wireless mesh strategies.