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.
Working outdoors means dealing with light variations. This is a known problem for outdoors applications and is even more crucial when detecting obstacles. In our case, besides the light variations, another issue has to be considered: the panel to be detected is black. It should be noticed that a surface that appears black does not reflect the visible light. As stated in @cite_29 , in order to detect the panel in such conditions, other techniques have to be adopted instead of exploiting vision data. For these reasons, we decided to exploit the laser range data (see ).
@cite_29
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This paper presents an experimented approach to autonomous robot navigation in an unknown natural environment. The approach involves several levels of reasoning, several environment representations, and three different motion modes. We focus on the “navigation level” of the whole system, which is in charge of reaching a distant goal by selecting sub-goals to reach, motion modes to apply, and perception tasks to execute for this purpose. We present how a terrain model dedicated to the navigation process is built on the 3D data acquired by the robot, and we describe an approach to tackle the difficult problem of planning perception and motion tasks. Experimental results on a realistic test site are presented and discussed.