This paper addresses the problem of pushing manipulation with nonholonomic mobile robots. Pushing is a fundamental skill that enables robots to move unwieldy objects that cannot be grasped. We propose a stable pushing method that maintains stiff contact between the robot and the object to avoid consuming repositioning actions. We prove that a line contact, rather than a single point contact, is necessary for nonholonomic robots to achieve stable pushing. We also show that the stable pushing constraint and the nonholonomic constraint of the robot can be simplified as a concise linear motion constraint. Then the pushing planning problem can be formulated as a constrained optimization problem using nonlinear model predictive control (NMPC). According to the experiments, our NMPC-based planner outperforms a reactive pushing strategy in terms of efficiency, reducing the robot's traveled distance by 23.8\% and time by 77.4\%. Furthermore, our method requires four fewer hyperparameters and decision variables than the Linear Time-Varying (LTV) MPC approach, making it easier to implement. Real-world experiments are carried out to validate the proposed method with two differential-drive robots, Husky and Boxer, under different friction conditions.

翻译：本文研究了非完整移动机器人的推动操作问题。推动是一种基本技能，使机器人能够移动无法抓取的笨重物体。我们提出了一种稳定推动方法，保持机器人与物体之间的刚性接触，以避免消耗重新定位动作。我们证明，对于非完整机器人实现稳定推动，线接触而非单点接触是必要的。我们还表明，稳定推动约束与机器人的非完整约束可简化为一个简洁的线性运动约束。然后，推动规划问题可被表述为一个使用非线性模型预测控制（NMPC）的约束优化问题。根据实验，我们的基于NMPC的规划器在效率上优于反应式推动策略，使机器人移动距离减少23.8%，时间减少77.4%。此外，与线性时变（LTV）MPC方法相比，我们的方法需要的超参数和决策变量减少了四个，因此更易于实现。我们使用两台差速驱动机器人Husky和Boxer，在不同摩擦条件下进行了真实世界实验，以验证所提出方法的有效性。