TC on Agricultural Robotics and Automation: Webinar

IEEE RAS TC on Agricultural Robotics and Automation   

Webinar #010

Title: Optimized Coverage Path Planning and Headland Turning Trajectory Optimization for Auto-Steer Agricultural Field Equipment

Speaker: Lie Tang, Iowa State University, USA

Abstract: With the rapid adoption of automatic guidance systems, automated path planning has great potential to further optimize the field efficiency of mobile agricultural equipment. Field operations should be done in a manner that minimizes time and travel over field surfaces and is coordinated with specific field operations, machine characteristics and topographical features of arable lands. To reach this goal, optimal coverage path planning (OCPP) is indispensable. This presentation will focus on the development of a set of core algorithms related to terrain modeling, field boundary representation, field decomposition, and optimal path searching based on objective functions appropriate to 2D and 3D terrains. Finally, a novel nonlinear optimization approach for headland turning trajectory optimization for a tractor with and without towed implement will be presented.

Bio: Dr. Tang is an associate professor in Department of Agricultural and Biosystems Engineering at Iowa State University (ISU). He received his BS in Electrical Engineering from Jiangsu University, an MS in Agricultural Engineering from Zhejiang University, and his PhD from University of Illinois at Urbana-Champaign. Before he assumed a faculty position at ISU, he worked in KU Leuven (Belgium) and was on faculty in both KVL (Denmark) and Wageningen University (Netherlands). Dr. Tang is currently continuing his research in developing advanced sensing, optimization and robotic technologies for agricultural production systems. Some recent research projects include optimized coverage path planning for agricultural field equipment on 2D and 3D terrains, robotic mechanical intra-row weeding for vegetable crops, high-throughput indoor and infield plant sensing and phenotyping using computer vision and robotic technologies, robust navigation control and headland turning optimization for agricultural robotic vehicles, hyperspectral imaging and computational intelligence for fungus detection, and real-time behavior monitoring systems for group-housed animals using 3D computer vision and RFID systems. Dr. Tang teaches undergraduate and graduate courses in Numerical Methods, Automation Systems, and Computational Intelligence.

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