基于MPC的分阶段规划，让机器人在杂乱环境中精准搬运物体
MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
- 先粗后精分阶段规划，逐步优化路径
- 融合视觉数据实时更新环境与动作规划
- 适合多机械臂在不确定杂乱环境作业
本文提出一种新型的粗到精运动规划框架，用于在未建模、杂乱环境中进行机器人操作。系统结合双摄像头感知与基于B样条的模型预测控制（MPC）方案。初始阶段，基于部分且不确定的观测生成可行全局轨迹；随着新视觉数据逐步融合，环境模型与运动规划持续优化。基于视觉的成本函数推动目标导向探索，改进的核感知器碰撞检测器实现高效约束更新，支持实时规划。该框架可处理闭链运动学结构，并支持动态重规划。在多机械臂平台上实验验证了其在不确定性与杂乱条件下的鲁棒性与适应性。
原文摘要 · Abstract (English)
This letter presents a novel coarse-to-fine motion planning framework for robotic manipulation in cluttered, unmodeled environments. The system integrates a dual-camera perception setup with a B-spline-based model predictive control (MPC) scheme. Initially, the planner generates feasible global trajectories from partial and uncertain observations. As new visual data are incrementally fused, both the environment model and motion planning are progressively refined. A vision-based cost function promotes target-driven exploration, while a refined kernel-perceptron collision detector enables efficient constraint updates for real-time planning. The framework accommodates closed-chain kinematics and supports dynamic replanning. Experiments on a multi-arm platform validate its robustness and adaptability under uncertainties and clutter.
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