Testing autonomous robots typically requires expensive test campaigns in the field. To alleviate them, a promising approach is to perform intensive tests in virtual environments. This paper presents an industrial case study on the feasibility and effectiveness of such an approach. The subject system is Oz, an agriculture robot for autonomous weeding. Its software was tested with weeding missions in virtual crop fields, using a 3D simulator based on Gazebo. The case study faced several challenges: the randomized generation of complex 3D environments, the automated checking of the robot behavior (test oracle), and the imperfect fidelity of simulation with respect to real-world behavior. We describe the test approach we developed, and compare the results with the ones of the industrial field tests. Despite the low-fidelity physics of the robot, the virtual tests revealed most software issues found in the field, including a major one that caused the majority of failures; they also revealed a new issue missed in the field. On the downside, the simulation could introduce spurious failures that would not occur in the real world.

中文翻译：

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绿野仙踪：在模拟中测试农业机器人

测试自主机器人通常需要在现场进行昂贵的测试活动。为了缓解它们，一种有前景的方法是在虚拟环境中执行密集测试。本文介绍了一个关于这种方法的可行性和有效性的工业案例研究。主题系统是Oz，一种用于自主除草的农业机器人。它的软件使用基于 Gazebo 的 3D 模拟器在虚拟农田中通过除草任务进行了测试。案例研究面临几个挑战：复杂 3D 环境的随机生成、机器人行为的自动检查（测试预言机）以及模拟对现实世界行为的不完美保真度。我们描述了我们开发的测试方法，并将结果与​​工业现场测试的结果进行比较。尽管机器人的物理保真度低，虚拟测试揭示了该领域发现的大多数软件问题，包括导致大多数故障的主要问题；他们还揭示了该领域遗漏的一个新问题。不利的一面是，模拟可能会引入在现实世界中不会发生的虚假故障。