标题： 基于MPC的杂乱环境中机器人物体运输的粗到精运动规划 显示英文标题

标题： MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments

摘要： 这封信提出了一种新的从粗到精的运动规划框架，用于在杂乱、未建模环境中的机器人操作。 系统集成了双摄像头感知设置与基于B样条的模型预测控制（MPC）方案。 最初，规划器从部分和不确定的观测中生成可行的全局轨迹。 随着新视觉数据的逐步融合，环境模型和运动规划逐步得到优化。 基于视觉的成本函数促进目标驱动的探索，而改进的核感知器碰撞检测器实现了实时规划的有效约束更新。 该框架支持闭链运动学并支持动态重新规划。 在多臂平台上的实验验证了其在不确定性和杂乱情况下的鲁棒性和适应性。 显示英文摘要

摘要： 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.