D4W: Dependable Data-Driven Dynamics for Wheeled Robots

TL;DR

D4W is a simulation framework that uses real-world sensor data to create accurate, dependable models of wheeled robot dynamics, enabling faster and more reliable development of control algorithms without extensive physical testing.

Contribution

The paper introduces D4W, a novel data-driven simulation framework that improves the accuracy of wheeled robot dynamics modeling compared to traditional analytical methods.

Findings

D4W achieves superior simulation accuracy over traditional approaches.

It enables rapid development and testing of control algorithms.

The framework is practical and integrates well with existing tools.

Abstract

Wheeled robots have gained significant attention due to their wide range of applications in manufacturing, logistics, and service industries. However, due to the difficulty of building a highly accurate dynamics model for wheeled robots, developing and testing control algorithms for them remains challenging and time-consuming, requiring extensive physical experimentation. To address this problem, we propose D4W, i.e., Dependable Data-Driven Dynamics for Wheeled Robots, a simulation framework incorporating data-driven methods to accelerate the development and evaluation of algorithms for wheeled robots. The key contribution of D4W is a solution that utilizes real-world sensor data to learn accurate models of robot dynamics. The learned dynamics can capture complex robot behaviors and interactions with the environment throughout simulations, surpassing the limitations of analytical methods, which only work in simplified scenarios. Experimental results show that D4W achieves the best simulation accuracy compared to traditional approaches, allowing for rapid iteration of wheel robot algorithms with less or no need for fine-tuning in reality. We further verify the usability and practicality of the proposed framework through integration with existing simulators and controllers.