Difference: FRCVPublicProject (12 vs. 13)

Robotics for small and family farms

By 2050, the global population is expected to reach 9.7 billion, challenging food production to keep pace. Agricultural robotics can help by addressing labor shortages, reducing costs, and promoting sustainable practices. Large agribusiness, monoculture farming facilitates robotic integration, however this farming method suffers from pest outbreaks and promotes soil depletion. Alternatively, robotics can support small and family farms, diversify agriculture, and enable competition with large agribusinesses. We will focus on a specific but significant requirement for this kind of application: the flexible and robust wide-area navigation required to handle the navigation of robots for livestock management despite the changing visual appearance of the landscape due to weather and growth of vegetation and crops (white paper with more motivating details whiteppr.pdf).

Consider a herding robot teamed with a drone to locate and retrieve livestock strayed from their herd. Herding activity covers a potentially large and dynamically changing area. Large area mapping is not an effective solution to this problem: it requires additional exploration which diverts the robots from herding; Dynamic obstacles like new rainstorm debris could in any case block previously mapped paths; It can be expected that GPS coverage will be limited such as in forested terrain, complicating map merging; Finally, at the mapped destination, the robot must still find the animals which may have moved.

With colleagues, Prof. Mohamed Rahouti and CS PhD. student Nasim Paykari, we propose an alternate, groundbreaking approach that transcends the limitations imposed by environmental variability, focusing on map-less, visual coordination between the robot and drone. This methodology further leverages dynamic environmental adaptation and a lightweight blockchain approach to enhance visual landmark recognition and navigation. By prioritizing visual cues and landmarks over purely map-based methods, our strategy facilitates robust, flexible navigation across changing agricultural landscapes. This approach not only addresses the immediate challenges of precision in location and task execution but also sets a new standard for the deployment of robotics in agriculture, particularly benefiting small and family farms by enabling them to maintain competitiveness and sustainability in the face of large agribusiness.