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TC AgRA – Webinar 36 – Bin Dog, A Self Propelled Platform for Bin Management in Orchards

Geoffrey A. Hollinger is an Assistant Professor in the Robotics Program and School of Mechanical, Industrial & Manufacturing Engineering at Oregon State University. His current research interests are in adaptive information gathering, distributed coordination, and learning for autonomous aerial, marine, and ground robotic systems. His past research includes networked underwater robotics at the University of Southern California, multi-robot search at Carnegie Mellon University, personal robotics at Intel Research Pittsburgh, active estimation at the University of Pennsylvania GRASP Laboratory, and miniature inspection robots for the Space Shuttle at NASA’s Marshall Space Flight Center. He received his Ph.D. (2010) and M.S. (2007) in Robotics from Carnegie Mellon University and his B.S. in General Engineering along with his B.A. in Philosophy from Swarthmore College (2005).​


Bin management during apple harvest season is an important activity for orchards. Typically, bin management is conducted by tractor-mounted forklifts or bin trailers. Before harvesting, tractor drivers need to estimate the number of bins required and place empty bins between tree rows accordingly. However, due to difficulty in the estimation, drivers still need to frequently replace full bins between tree rows with empty bins so pickers can pick apples continuously. In order to simplify this work process and improve work efficiency of bin management, we propose the concept of a self-propelled “bin-dog” system. This system was designed with a “go-over-the-bin” feature, which allows it to drive over bins between tree rows. To validate this system concept, a prototype was designed and built. Field tests in a commercial orchard showed that when a full bin was placed 50 m away from the entrance of a lane, the prototype system could replace a full bin with an empty bin in 354 s. Simulated trials with multiple vehicles in the orchard environment show potential time savings of up to 30% versus conventional bin-management methods.​


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