Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction control-based instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness.

翻译：由于运动学和动力学中的不确定性，当前移动机器人（如球形机器人）的轨迹跟踪框架无法在多种地形（尤其是崎岖及未知地形）上有效运行。鉴于这是机器人在野外执行任务的必要前提，我们改进了先前提出的分层轨迹跟踪框架以解决该问题。首先，提出了一种改进的自适应径向基函数神经网络（RBFNN）来表示运动动力学中的所有不确定性。随后利用李雅普诺夫函数设计其自适应律，并在权重更新过程中引入变步长算法以加速收敛并提升稳定性。由此提出了一种新型基于自适应模型预测控制的指令规划器（VAN-MPC）。在不修改底层控制器的情况下，我们通过采用新型指令规划器VAN-MPC最终构建了多地形轨迹跟踪框架。实际实验验证了该框架的有效性与鲁棒性。