提出一种快速安全的机器人操控鲁棒控制方法，可应对模型不确定性和障碍物碰撞。
Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators
- 基于鲁棒管状MPC与路径规划，实现凸优化求解
- 在6自由度机器人上验证，支持更高模型不确定性容忍度
- 适合工业场景中高精度、高速度避障任务
工业机械臂常在杂乱环境中运行，安全运动规划至关重要。但模型不确定性使该任务更复杂，导致需设置保守速度限制以降低扰动影响。因此亟需能保证安全且高速执行的控制方法。本文针对此问题，提出一种新型模型预测控制（MPC）方案，核心包含鲁棒管状MPC和走廊规划算法，用于生成无碰撞轨迹。该方法可转化为凸MPC形式，计算速度快，具备实际应用价值。我们在模拟环境中对具有6自由度的工业机器人进行了测试，环境存在模型参数不确定性且障碍物密集。结果表明，本方法在容忍更高模型不确定性的同时，实现了更快运动速度，优于基准方法。
原文摘要 · Abstract (English)
Industrial manipulators typically operate in cluttered environments, where safe motion planning is critical. However, model uncertainties further complicate this task, which leads to conservative speed limits to reduce the influence of disturbances. Hence, there is a need for control methods that can guarantee safe motions which are executed fast. We address this by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC formulation, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a 6 DOF industrial robot operating in cluttered environments with uncertain model parameters. We outperform benchmark methods by tolerating higher levels of model uncertainty while achieving faster motion.
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