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A robotic solution for the unmanned ground vehicles (UGVs) to execute thehighly complex task of object manipulation in an autonomous mode is presented.This paper primarily focuses on developing an autonomous robotic system capableof assembling elementary blocks to build the large 3D structures in GPS-deniedenvironments. The key contributions of this system paper are i) Designing of adeep learning-based unified multi-task visual perception system for objectdetection, part-detection, instance segmentation, and tracking, ii) anelectromagnetic gripper design for robust grasping, and iii) system integrationin which multiple system components are integrated to develop an optimizedsoftware stack. The entire mechatronic and algorithmic design of UGV for theabove application is detailed in this work. The performance and efficacy of theoverall system are reported through several rigorous experiments.

Visually Guided UGV for Autonomous Mobile Manipulation in Dynamic and Unstructured GPS Denied Environments