Affine Transformable Unmanned Ground Vehicle

TL;DR

This paper introduces a novel affine transformable unmanned ground vehicle (ATUGV) capable of safe, aggressive deformation and payload carrying, using deep learning and control strategies to enable reconfigurable affine transformations validated through experiments.

Contribution

It presents a new design for an affine transformable UGV with deep neural network-based cell interconnection and control methods for safe deformation and payload management.

Findings

Successfully demonstrated affine transformation tracking in hardware experiments.

Deep neural network effectively structures cell interconnections for deformation.

The ATUGV can safely perform aggressive deformations while carrying multiple payloads.

Abstract

This paper develops the proof of concept for a novel affine transformable unmanned ground vehicle (ATUGV) with the capability of safe and aggressive deformation while carrying multiple payloads. The ATUGV is a multi-body system with mobile robots that can be used to power the ATUGV morphable motion, powered cells to enclose the mobile robots, unpowered cells to contain payloads, and a deformable structure to integrate cells through bars and joints. The objective is that all powered and unpowered cells motion can safely track a desired affine transformation, where an affine transformation can be decomposed into translation, rigid body rotation, and deformation. To this end, the paper first uses a deep neural network to structure cell interconnection in such a way that every cell can freely move over the deformation plane, and the entire structure can reconfigurably deform to track a desired affine transformation. Then, the mobile robots, contained by the powered cells and stepper motors, regulating the connections of the powered and unpowered cells, design the proper controls so that all cells safely track the desired affine transformation. The functionality of the proposed ATUGV is validated through hardware experimentation and simulation.