Pushing is a simple yet effective skill for robots to interact with and further change the environment. Related work has been mostly focused on utilizing it as a non-prehensile manipulation primitive for a robotic manipulator. However, it can also be beneficial for low-cost mobile robots that are not equipped with a manipulator. This work tackles the general problem of controlling a team of mobile robots to push collaboratively polytopic objects within complex obstacle-cluttered environments. It incorporates several characteristic challenges for contact-rich tasks such as the hybrid switching among different contact modes and under-actuation due to constrained contact forces. The proposed method is based on hybrid optimization over a sequence of possible modes and the associated pushing forces, where (i) a set of sufficient modes is generated with a multi-directional feasibility estimation, based on quasi-static analyses for general objects and any number of robots; (ii) a hierarchical hybrid search algorithm is designed to iteratively decompose the navigation path via arc segments and select the optimal parameterized mode; and (iii) a nonlinear model predictive controller is proposed to track the desired pushing velocities adaptively online for each robot. The proposed framework is complete under mild assumptions. Its efficiency and effectiveness are validated in high-fidelity simulations and hardware experiments. Robustness to motion and actuation uncertainties is also demonstrated.

翻译：推动是机器人与环境交互并进一步改变环境的一种简单而有效的技能。相关研究主要集中于将其作为机器人操作器的非抓取式操作基元。然而，对于未配备操作器的低成本移动机器人而言，推动同样具有重要价值。本研究致力于解决在复杂障碍物密集环境中，控制一组移动机器人协作推动多面体物体的通用问题。该问题包含了接触密集型任务的若干特征性挑战，例如不同接触模式间的混合切换，以及因接触力受限导致的欠驱动特性。所提出的方法基于对可能接触模式序列及相关推动力的混合优化，其中：(i) 通过对任意数量机器人推动通用物体的准静态分析，结合多方向可行性评估生成一组充分接触模式；(ii) 设计分层混合搜索算法，通过圆弧段迭代分解导航路径并选择最优参数化模式；(iii) 提出非线性模型预测控制器，为每个机器人自适应在线跟踪期望推动速度。该框架在温和假设下具有完备性。通过高保真仿真与硬件实验验证了其效率与有效性，并展示了其对运动与驱动不确定性的鲁棒性。