This paper describes RUR53, the unmanned mobile manipulator robot developed by the Desert Lion team of the University of Padova (Italy), and its experience in Challenge 2 and the Grand Challenge of the first Mohamed Bin Zayed International Robotics Challenge (Abu Dhabi, March 2017). According to the competition requirements, the robot is able to freely navigate inside an outdoor arena; locate and reach a panel; recognize and manipulate a wrench; use this wrench to physically operate a valve stem on the panel itself. RUR53 is able to perform these tasks both autonomously and in teleoperation mode. The paper details the adopted hardware and software architectures, focusing on its key aspects: modularity, generality, and the ability of exploiting sensor feedback. These features let the team rank third in the Gran Challenge in collaboration with the Czech Technical University in Prague, Czech Republic, the University of Pennsylvania, USA, and the University of Lincoln, UK. Tests performed both in the Challenge arena and in the lab are presented and discussed, focusing on the strengths and limitations of the proposed wrench and valve classification and recognition algorithms. Lessons learned are also detailed.
Competitions are increasingly spreading across the robotics community. The reason is that they encourage innovation in the resolution of complex real-world problems while going beyond the typical research laboratory scenarios: very challenging experimental setups are usually provided, characterized by uncontrolled and not easily reproducible conditions. Participants have to face these challenges by applying a combination of state of the art algorithms, innovative methods, ideas and or prototype systems from industrial R &D @cite_2 . An example is the DARPA Robotics Challenge (DRC) @cite_19 : it aimed to develop semi-autonomous ground robots, specifically humanoids, able to perform complex tasks in dangerous, degraded environments, such as disaster or emergency-response scenarios. Another interesting scenario is the one proposed by the Amazon Picking Challenge (APC) @cite_21 : it assigned robots simplified versions of tasks that humans commonly face in warehouses, such as picking items from shelves and putting them into containers, and vice-versa.
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This paper summarizes lessons learned from the first Amazon Picking Challenge in which 26 international teams designed robotic systems that competed to retrieve items from warehouse shelves. 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. Reports on the significance of sponsoring robot competitions and discusses what we learn from their outcomes.