Robotics 8970 Colloquium: Parikshit Maini
Mobile Robots in Agriculture
The presentation will start with an overview of some of the recent projects and research in agricultural robotics in the Robotic Sensor Networks Lab. Dr. Parikshit will then talk about our recent work on weed removal in organic dairy pastures using autonomous robots. The carbon footprint of using diesel-run farm vehicles for weed removal and other agricultural tasks has been a cause of concern, especially in the case of organic farms that do not use chemicals. Combined with the knowledge that one third of all land in the mainland US is used for cattle grazing, this problem holds considerable significance. The lab has designed an autonomous battery-powered mobile robot, called Cowbot, for weed control in the rough and challenging environment on cow pastures. Cow pastures are usually open fields and there is large variation in weed population with geographic location and time of the year. He will then present their work on two interesting research questions: budget-aware weed detection using aerial imagery and online trajectory planning for the Cowbot to efficiently use weed detection information.
Traditionally, detection and planning have been addressed as separate problems that do not account for the range of operation of mobile robots. This separation leads to mobile robots either completing only a part of the operation or needing to refuel and resume operations. He will will present our work on weed detection from aerial imagery that accounts for the available planning budget of the autonomous mower. The second problem addresses online trajectory planning for the Cowbot with a limited field of view of onboard sensors and a finite turning radius. Given an onboard weed detection module, efficiently using detection information in real time to plan robot trajectories is challenging. Due to the unknown and variable weed density on pastures, coverage paths can lead to large wastage of resources. I will present reactive planning algorithms to compute efficient robot trajectories that utilize detection information from onboard sensing systems. They have deployed these algorithms on the Cowbot and have evaluated them in large scale experiments on cow pastures. He will then show videos of the Cowbot in action and talk about future directions that we are pursuing in this space.
About Parikshit Maini
Parikshit Maini is a Post-Doctoral Associate in the Department of Computer Science and Engineering at University of Minnesota and a member of the Robotic Sensor Networks lab headed by Prof. Volkan Isler. He works in the area of field robotics and applied AI with a focus on environmental and agricultural applications for mobile robot systems. He is leading the planning and navigation team on the "Cowbot - autonomous weed mower" project that has been covered in multiple news media stories (PBS, Star Tribune, Rural Media Group) and was recently showcased in live demos at the Minnesota FarmFest 2021. He also works on cooperative planning for heterogeneous multi-robot systems. He has developed planning algorithms for large-scale area coverage, persistent monitoring and visibility-based monitoring on terrains using cooperative aerial and ground robotic sensor nodes.
He holds a PhD in Computer Science and Engineering from Indraprastha Institute of Information Technology-Delhi, India. He also holds a M.Tech. degree in Computer Science and Engineering from IIIT-Delhi and a B.Tech. in Information Technology from Guru Gobind Singh Indraprastha University, Delhi in India.