用贝塞尔曲线高效表示双臂机械臂运动轨迹,实现在线柔顺操作。
An Efficient Representation of Whole-body Model Predictive Control for Online Compliant Dual-arm Mobile Manipulation
- 用贝塞尔曲线参数化双臂轨迹,加速长时序规划。
- 在短时域内满足全肢体硬约束,实现柔顺控制与避障。
- 适合高维空间实时控制,适用于复杂动态环境下的双臂机器人。
双臂移动操作臂可使用简单末端执行器搬运大尺寸物体。为在动态环境中安全、柔顺地交互,需在线完成高冗余移动操作臂的全身运动规划,并满足多种硬约束,这极具挑战性。本文提出一种高效的全身运动轨迹表示方法,集成于双层模型预测控制(MPC)框架中。第一层MPC利用贝塞尔曲线参数化两个协作末端执行器的优化无碰撞轨迹,在SE(3)空间中实现快速长时序面向任务的运动规划,同时考虑近似可行性约束。该方法进一步用于第二层MPC中对全身轨迹的参数化,实现短时域内的全身运动生成与预测阻抗控制,同时满足全身硬约束。该表示使两层MPC均具备连续性,避免了传统离散化MPC中的模型状态转移误差和密集决策变量设置。该方法增强了双层MPC在高维空间中的在线执行能力,生成一致的指令以驱动混合位置/速度控制机器人。仿真对比与真实实验表明,该方法在静态与动态障碍物避障及对被操作物体和外部扰动的柔顺交互控制中均表现出高效性与鲁棒性。
原文摘要 · Abstract (English)
Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient representation of whole-body motion trajectories within our bilevel model-based predictive control (MPC) framework. We utilize Bézier-curve parameterization to represent the optimized collision-free trajectories of two collaborating end-effectors in the first MPC, facilitating fast long-horizon object-oriented motion planning in SE(3) while considering approximated feasibility constraints. This approach is further applied to parameterize whole-body trajectories in the second MPC for whole-body motion generation with predictive admittance control in a relatively short horizon while satisfying whole-body hard constraints. This representation enables two MPCs with continuous properties, thereby avoiding inaccurate model-state transition and dense decision-variable settings in existing MPCs using the discretization method. It strengthens the online execution of the bilevel MPC framework in high-dimensional space and facilitates the generation of consistent commands for our hybrid position/velocity-controlled robot. The simulation comparisons and real-world experiments demonstrate the efficiency and robustness of this approach in various scenarios for static and dynamic obstacle avoidance, and compliant interaction control with the manipulated object and external disturbances.
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