Affine Transformable Unmanned Ground Vehicle

2026-02-07

AI Summary

This work proposes an affinely transformable multi-body system architecture (ATUGV) for unmanned ground vehicles tasked with complex missions requiring both safety and agile maneuvering while carrying multiple payloads. By integrating powered drive units, unpowered payload modules, and reconfigurable interconnecting structures, the system enables coordinated tracking of target affine transformations—including translation, rotation, and shape deformation. A deep neural network is employed to plan the interconnection topology among units, while stepper motors modulate connection states in concert with mobile chassis actuation. A dedicated control strategy coordinates global locomotion and morphological adaptation. Hardware experiments and simulations demonstrate that ATUGV can safely and accurately execute complex affine deformation maneuvers under multi-payload conditions, significantly enhancing the platform’s morphological adaptability and mission flexibility.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• affine transformation
• unmanned ground vehicle
• deformable structure
• multi-body system
• payload transportation

Innovation

• affine transformable
• reconfigurable multi-body system
• deep neural network for interconnection
• morphable unmanned ground vehicle
• deformation control