Analysis and Observations from the First Amazon Picking Challenge
This addresses the problem of automating warehouse picking tasks for logistics and robotics industries, but it is incremental as it focuses on summarizing existing competition data.
The paper analyzed the first Amazon Picking Challenge, where 26 teams designed autonomous robots to pick items from warehouse shelves, and reported survey results to identify trends and correlate them with competition success.
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.