yl6809永利官网机器人与信息自动化研究所 天津市智能机器人技术重点实验室
Institute of Robotics and Automatic Information System
Tianjin Key Laboratory of Intelligent Robotics
2024年春季先进机器人与人工智能系列学术讲座（第253期）
Seminar Series：Advanced Robotics & Artificial Intelligence
报告时间：2024年7月1日（周一）10:00~12:00
报告嘉宾：Prof. Dezhen Song, Texas A&M University
报告题目：Toward Robotic Weed Removal
报告摘要：
In this talk, I will report on our recent progress in developing algorithms and systems to perform robotic weed removal tasks in precision agriculture. Weed removal is an eternal issue in agriculture. However, the task is often labor intensive or has a large environmental impact if herbicide is used. Using robots can address those issues and make it possible to provide an environmentally friendly approach in weed management. Here, we present three case studies. In the first case, we developed a new deep learning method to effectively distinguish nutsedge weeds from background turf grass. The challenge is to reduce human data labeling costs by designing a new network and features and combining synthetic data that enable network training with a small amount of data with low labeling effort. In the second case, we will discuss how we design a robotic micro-volume weed spraying system and motion planning algorithms to suppress weeds in early growth stage. If time permits, I will discuss our latest progress in weed flaming by employing a mobile manipulator with a SPOT mini quadruped robot and a 6-degree of freedom (DoF) robot.
报告人简介：
Dezhen Song is a Professor and Deputy Department Chair with Department of Robotics in MBZ University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE, and a Professor (on leave) and former Associate Department Head for Academics with Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. Song received his Ph.D. in 2004 from University of California, Berkeley; MS and BS from Zhejiang University in 1998 and 1995, respectively. Song's primary research area is robot perception, networked robots, visual navigation, automation, and stochastic modeling. From 2008 to 2012, Song was an Associate Editor of IEEE Transactions on Robotics (T-RO). From 2010 to 2014, Song was an Associate Editor of IEEE Transactions on Automation Science and Engineering (T-ASE). Song was a Senior Editor for IEEE Robotics and Automation Letters (RA-L) from 2017 to 2021 and currently is a Senior Editor for IEEE Transactions on Automation Science and Engineering (T-ASE). He is also a multimedia editor and chapter author for Springer Handbook of Robotics. His research has resulted in one monograph and more than 138 refereed conferences and journal publications. Dr. Song received the NSF Faculty Early Career Development (CAREER) Award in 2007, the Kayamori Best Paper Award of the 2005 IEEE International Conference on Robotics and Automation (ICRA), the 2022 Best Paper Awards of the LCT 2022 and 2024 Affiliated Conference, the 1st place in the GM/SAE AutodriveChallenge II competition in 2023, and the Amazon Research Award in 2020.
