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.

翻译：轮式机器人因其在制造业、物流和服务业中的广泛应用而受到极大关注。然而，由于难以建立高精度的轮式机器人动力学模型，为其开发与测试控制算法仍然具有挑战性且耗时，需要大量的物理实验。为解决此问题，我们提出D4W（面向轮式机器人的可靠数据驱动动力学模型），这是一个融合数据驱动方法的仿真框架，旨在加速轮式机器人算法的开发与评估。D4W的核心贡献在于提出了一种利用真实世界传感器数据学习精确机器人动力学模型的解决方案。学习得到的动力学模型能够在仿真过程中捕捉复杂的机器人行为及环境交互，克服了仅适用于简化场景的解析方法的局限性。实验结果表明，与传统方法相比，D4W实现了最佳的仿真精度，使得轮式机器人算法能够快速迭代，且无需或仅需少量现实世界中的精细调优。我们通过将该框架与现有仿真器及控制器集成，进一步验证了其可用性与实用性。