Abstract-This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge.Note to Practitioners: Abstract-Perception, motion planning, grasping, and robotic system engineering has reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semi-structured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.

Bin picking is still a challenge in robotics, as patent in recent robot competitions. These competitions are an excellent platform for technology comparisons since some participants may use state-of-theart technologies, while others may use conventional ones. Nevertheless, even though points are awarded or subtracted based on the performance in the frame of the competition rules, the final score does not directly reflect the suitability of the technology. Therefore, it is difficult to understand which technologies and their combination are optimal for various real-world problems. In this paper, we propose a set of performance metrics selected in terms of actual field use as a solution to clarify the important technologies in bin picking. Moreover, we use the selected metrics to compare our four original robot systems, which achieved the best performance in the Stow task of the Amazon Robotics Challenge 2017. Based on this comparison, we discuss which technologies are ideal for practical use in bin picking robots in the fields of factory and warehouse automation.

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This paper presents an automated fish fry counting by detecting the pixel area occupied by each fish silhouette using image processing. A photo of the fish fry in a specially designed container undergoes binarization and edge detection. For every image frame, the total fish count is the sum of the area inside every contour. Then the average number of fishes for every frame is summed up. Experimental data shows that the accuracy rate of the method reaches above 95 percent for a school of 200, 400, 500, and 700 fish fry. To minimize errors due to crowding in the container, schooling behavior analysis is considered. The behavioral effects of different colored lights on milkfish and tilapia are thoroughly investigated. The system's effectiveness, efficiency, possible improvements, and other potential applications are discussed.