Closely tracking a defined path by a wheeled mobile robot on a three-dimensional surface is important for accurate movement on uneven terrain. Conventional methods in two dimensions are difficult to extend to three dimensions due to the computational complexity in finding wheel-terrain interactions. Learning based methods bypass the need for explicit modelling and can accurately predict these dynamic relations. We use learning based Model Predictive Controller (MPC) for path tracking by a four-wheel robot. A neural network is used as a model due to its capability for learning complex state transition dynamics. Learning terrain height information aids the MPC on uneven terrain. The algorithm is rigorously tested in simulation on a variety of terrain profiles to track paths by a four wheel robot's center of mass. Results show the method is robust to model errors and that our novel method of incorporating terrain height information significantly improves performance on terrains with high frequency surface profile changes.

中文翻译：

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添加地形高度以改进四轮机器人在不平坦地形上进行路径跟踪的模型学习


轮式移动机器人在三维表面上密切跟踪定义的路径对于在不平坦的地形上精确移动非常重要。由于寻找车轮与地形相互作用的计算复杂性，二维的传统方法很难扩展到三维。基于学习的方法不需要显式建模，并且可以准确地预测这些动态关系。我们使用基于学习的模型预测控制器（MPC）来进行四轮机器人的路径跟踪。神经网络因其学习复杂状态转换动态的能力而被用作模型。学习地形高度信息有助于 MPC 在不平坦的地形上进行操作。该算法在各种地形轮廓的模拟中经过严格测试，以跟踪四轮机器人质心的路径。结果表明，该方法对模型误差具有鲁棒性，并且我们结合地形高度信息的新颖方法显着提高了具有高频表面轮廓变化的地形的性能。