A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can easily identify if an arm is pointing in some general direction. The Sawyer robotic arm is used for developing and training this intelligent algorithm. Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to cover the entire joint configuration space. In this work, orthogonal arrays are used, similar to the Taguchi method, to efficiently span the joint space with the minimal number of training images. This ``optimally'' generated database is used to train the custom ANN and its degree of accuracy is on average equal to twice the smallest joint displacement step used for database generation. A pre-trained ANN will be useful for estimating the postures of robotic manipulators used on space stations, spacecraft, and rovers as an auxiliary tool or for contingency plans.
翻译:美国航天局为了解这些天体的地质和构成而做出的努力在很大程度上依赖于机器人武器的使用。当人类与机器人探索者一起工作时,安全和冗余方面至关重要。此外,机器人武器对于卫星服务和计划中的轨道碎片缓减任务至关重要。这项工作的目标是建立一个基于月球和火星的大量机器人和人类辅助飞行任务。美国航天局努力从一个(RGB-D)图像中迅速识别7度自由机器人臂(DoF)的姿势,这与人类一样,可以很容易地识别一个手臂指向某种一般方向。当人类与机器人探索者一起工作时,安全和冗余方面将是至关重要的。此外,机器人武器对于卫星服务和计划中的轨道碎片缓减任务至关重要。由于Sawyer的联合空间范围跨于7个层面,因此覆盖整个联合配置空间是一项无法完成的任务。在这项工作中,与Taguchi方法相似,使用圆形阵列将联合空间的姿势与培训图像的最少数量相隔开来。这个“Patimitelylylyly”生成的航天器数据库将用来为ASMANSM的模模模模模模模模模模模模模模模模模模模模。用来对A的模范数据库进行测试前的模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模的模模模模模模模模模模模模模的模的模模的模的模的模的模模模的模的模的模的模的模的模模模模的模模模模模模的模模模模模模模模的模模模模模模模化,将用来训练。将用来训练。用来训练。将用来训练。