A robotic solution for the unmanned ground vehicles (UGVs) to execute the highly complex task of object manipulation in an autonomous mode is presented. This paper primarily focuses on developing an autonomous robotic system capable of assembling elementary blocks to build the large 3D structures in GPS-denied environments. The key contributions of this system paper are i) Designing of a deep learning-based unified multi-task visual perception system for object detection, part-detection, instance segmentation, and tracking, ii) an electromagnetic gripper design for robust grasping, and iii) system integration in which multiple system components are integrated to develop an optimized software stack. The entire mechatronic and algorithmic design of UGV for the above application is detailed in this work. The performance and efficacy of the overall system are reported through several rigorous experiments.
翻译:本文主要侧重于开发一个自主的机器人系统,能够组装基本部件,以便在GPS封闭的环境中建造大型的3D结构。本系统文件的主要贡献是:(一) 设计一个基于深层次学习的统一多任务视觉系统,用于物体探测、部分探测、例分解和跟踪;(二) 一个用于稳健捕捉的电磁控制器设计;以及(三) 系统集成,将多个系统组件整合在一起,以开发一个优化的软件堆。这项工作详细介绍了UGV用于上述应用的整个机械学和算法设计。通过若干严格的实验,报告整个系统的性能和功效。