Bin management during apple harvest season is an important activity for orchards. Typically, empty and full bins are handled by tractor-mounted forklifts or bin trailers in two separate trips. In order to simplify this work process and improve work efficiency of bin management, the concept of a robotic bin-dog system is proposed in this study. This system is designed with a "go-over-the-bin" feature, which allows it to drive over bins between tree rows and complete the above process in one

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... To validate this system concept, a prototype and its control and navigation system were designed and built. Field tests were conducted in a commercial orchard to validate its key functionalities in three tasks including headland turning, straight-line tracking between tree rows, and "go-over-the-bin." Tests of the headland turning showed that bin-dog followed a predefined path to align with an alleyway with lateral and orientation errors of 0.02 m and 1.5 • . Tests of straight-line tracking showed that bin-dog could successfully track the alleyway centerline at speeds up to 1.00 m·s −1 with a RMSE offset of 0.07 m. The navigation system also successfully guided the bin-dog to complete the task of go-over-the-bin at a speed of 0.60 m·s −1 . The successful validation tests proved that the prototype can achieve all desired functionality. Robotics 2017, 6, 12 2 of 17 greater challenge to the tree fruit industry in the U.S. due to its heavy use of labor during the harvest season. Furthermore, labor shortages threaten the viability of the tree fruit industry in the long term [2] . Innovation is critical to overcoming this problem and maintaining the industry's competitiveness in the global marketplace. Over the past few decades, numerous utility platforms have been developed to improve the productivity of orchard operations [3] [4] [5] [6] . Most of these platforms still rely on human operators to maneuver for orchard traversing. Autonomous vehicles have long been a solution used to ease the pressure of labor issues on agricultural activities. Due to the development of computing units and sensor technologies and efforts from numerous researchers, Global Positioning Systems (GPS) and vision-guided vehicles have been widely implemented on farming equipment [7-10]. Current high-end commercial GPS-based navigation systems can reduce the path tracking error to less than 0.03 m [11] , which is reported to be superior to manually-steered system in terms of driving accuracy [12] . Despite its extensive applications in farming, autonomous platforms for orchard operations are still relatively rare due to unstable GPS signals in orchard environments [13] . Alternative perception sensors to GPS for orchard navigation are local sensors such as laser scanner and vision sensors. Due to the relatively high accuracy and invariance, laser scanners have been widely applied to measure tree canopy geometry [14-18] and map orchards [19] . Furthermore, in modern high-density orchards, fruit trees are planted in straight rows, which makes it ideal to use local sensors to guide robotic platforms operating in such environments. Barawid et al. [20] used a 2D laser scanner to guide a tractor to follow the centerline of tree alleyways in an orchard. An autonomous navigation system developed by Subramanian and Burks [21] used multiple sensors including a laser scanner, a video camera, and an inertial measurement unit (IMU) to guide a vehicle in a citrus orchard. It can drive along tree alleyways with a mean lateral error of 0.05 m at a speed of 3.1 m·s −1 and steer into the next alleyway at the end of rows. Hamner et al. [22, 23] developed an automated platform guided via two 2D-laser scanners, which has already completed over 350 km for orchard traversals [24] . Moorehead et al. [25] presented a multi-tractor system for mowing and spraying in a citrus orchard. Each autonomous tractor in the system used a 2D-laser and a color camera for environment perception. These tractors have successfully driven more than 1500 km. Systems developed in previous studies are mainly used as assist platforms that can carry or follow workers and traverse tree alleyways. These studies focus on relatively simple driving tasks such as straight line driving between tree rows and row entry. Despite all the developed technologies for orchard operations, currently, no robotic platforms for bin management are commercially available. Our long-term goal is to develop a robust multi-robotic bin management system implementable in the natural environment of tree fruit orchards. This article focuses on the development of a novel bin-dog system capable of robotic bin management. The entire system includes two major components: (1) A bin-dog prototype that can deliver an empty bin and remove a full bin from an alleyway in one trip, and (2) A control and navigation system to assist the bin-dog to complete bin-managing tasks automatically inside an orchard. Material & Methods Design of the Bin-Dog Prototype To test the proposed concept, it is essential to design a research prototype of the bin-dog robot capable of handling bins in a natural orchard environment. Such a prototype should perform the critical functions of (1) carrying a 1.20 m × 1.20 m × 1.00 m fruit bin and traveling at a 1.2 m·s −1 maximum speed within a 2.40 m tree alleyway in typical Pacific Northwest (PNW) high-density tree fruit orchards; (2) carrying an empty bin to go over a full bin laid in alleyway, and placing an empty bin at a target location in the harvesting zone; and (3) driving a full bin, weighing up to 400 kg, back from the harvesting zone to the designated bin collecting area outside the alleyway, all in one trip. To do so, the bin-dog prototype uses a "go-over-the-bin" function, which allows it to drive over a full bin between tree rows. Robotics 2017, 6, 12 3 of 17 As illustrated in Figure 1 , the system manages bins in a five-step process. It can (1) load an empty bin in the collection station and drive it into an alleyway until reaching the full bin, (2) lift the empty bin and drive over the full bin, (3) continue to the target spot and place the empty bin, (4) drive back to load the full bin, and (5) drive the loaded full bin out of the alleyway to the collection station. By combining two trips into one, such a process has the potential to greatly reduce the travel distance and time, significantly improving overall efficiency.