基于智能预测的力/位混合控制方法

针对机器人在形状未知接触环境表面上进行柔顺力控制问题,在传统的力/位混合控制模型中采用一种智能预测算法,此算法通过三种预测因子预测并调整未来采样时刻的力/位混合控制模型中的期望轨迹,并考虑环境曲率和刚度变化的特点.为了验证预测算法的有效性,构建了开放式的机器人力控制系统,在不同期望力、不同跟踪速度下对非规则受限表面进行了力控制实验研究,分析了受限系统中未知环境参数对接触力的影响.实验结果证明该方法对未知接触环境的变化具有较强的适应能力.在实验条件下,稳定状态下的力控制误差可以控制在3%之内.

Hybrid Position/Force Control Based on Intelligent Prediction

An intelligent prediction algorithm to be introduced in conventional hybrid position/force control model is developed to deal with the force-controlled execution of compliant robot tasks in an unknown environment.The algorithm makes use of the three factors to predict and readjust the expected trajectory given in hybrid position/force control model in future sampling,with the environmental change in curvature and stiffness taken into account.To verify the effectiveness of the proposed algorithm,an open architecture platform of robot force control is set up to investigate experimentally the force control on irregularly curved surfaces for different expected forces and different tracking velocities.The influences of unknown environmental parameters on the contact force are analyzed on the basis of experimental data.The experimental results verified that the way proposed for intelligent prediction has high adaptability to unknown environment in changing.The error of force controlling is within 3% in constrained motion.