提出新框架，让机器人推车更灵活抗干扰。
Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances
- 用局部坐标和新运动模型优化推车路径，提升灵活性。
- 无需精确动力学模型，就能有效抑制干扰并降低误差。
- 首个系统评估推车灵活性与鲁棒性的实测工作，适合移动机器人研究者。
移动机器人在执行精确灵活的推车任务时面临挑战：推车过程中的运动约束与机器人冗余导致复杂的运动规划问题，而可变载荷和扰动则带来复杂的动力学特性。本文提出一种新型规划与控制框架，实现全臂协调与鲁棒自适应控制。运动规划方法采用局部坐标表示和新颖的运动学模型，求解非线性优化问题，生成可行且灵活的推车姿态。此外，提出一种扰动抑制控制方法，在无需精确动态模型的前提下，有效抵抗扰动并减少控制误差。通过大量仿真与真实场景实验验证，本方法显著优于现有方法。据我们所知，这是首个在实验中系统评估推车方法灵活性与鲁棒性的研究。视频补充材料见 https://sites.google.com/view/mpac-pushing/。
原文摘要 · Abstract (English)
Precise and flexible cart-pushing is a challenging task for mobile robots. The motion constraints during cart-pushing and the robot's redundancy lead to complex motion planning problems, while variable payloads and disturbances present complicated dynamics. In this work, we propose a novel planning and control framework for flexible whole-body coordination and robust adaptive control. Our motion planning method employs a local coordinate representation and a novel kinematic model to solve a nonlinear optimization problem, thereby enhancing motion maneuverability by generating feasible and flexible push poses. Furthermore, we present a disturbance rejection control method to resist disturbances and reduce control errors for the complex control problem without requiring an accurate dynamic model. We validate our method through extensive experiments in simulation and real-world settings, demonstrating its superiority over existing approaches. To the best of our knowledge, this is the first work to systematically evaluate the flexibility and robustness of cart-pushing methods in experiments. The video supplement is available at https://sites.google.com/view/mpac-pushing/.
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