Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety is investigated in this work through various contact reactions on a real planar PR. External forces are estimated based on proprioceptive information and a dynamics model, which allows contact detection. Retraction along the direction of the estimated line of action provides an instantaneous response to limit the occurring contact forces within the experiment to 70N at a maximum velocity 0.4m/s. A reduction in the stiffness of a Cartesian impedance control is investigated as a further strategy. For clamping, a feedforward neural network (FNN) is trained and tested in different joint angle configurations to classify whether a collision or clamping occurs with an accuracy of 80%. A second FNN classifies the clamping kinematic chain to enable a subsequent kinematic projection of the clamping joint angle onto the rotational platform coordinates. In this way, a structure opening is performed in addition to the softer retraction movement. The reaction strategies are compared in real-world experiments at different velocities and controller stiffnesses to demonstrate their effectiveness. The results show that in all collision and clamping experiments the PR terminates the contact in less than 130ms.

翻译：并联机器人因其低运动质量而具备实现人机安全协作的潜力。但由于并联运动链结构，在运动链中发生碰撞和夹持形式接触的风险增加。本研究通过在实际平面并联机器人上实施多种接触反应策略来探究安全性保障问题。基于本体感知信息与动力学模型对外部力进行估计，从而实现接触检测。沿估计作用线方向进行回缩运动，可在实验中以0.4m/s的最大速度将接触力限制在70N以内，提供即时响应。作为另一种策略，研究了笛卡尔阻抗控制刚度降低的效果。针对夹持情况，训练了一个前馈神经网络并在不同关节角构型下进行测试，以80%的准确率区分碰撞或夹持事件。第二个前馈神经网络对夹持运动链进行分类，从而将夹持关节角通过运动学投影转换至旋转平台坐标系。通过这种方式，在更柔顺的回缩运动基础上实现了结构展开。在不同速度和控制器刚度条件下的实际实验中，对上述反应策略进行了比较，验证其有效性。结果表明，在所有碰撞和夹持实验中，并联机器人均能在130ms内终止接触。