Supplementary data for "Velocity Estimation of Robot Manipulators: An Experimental Comparison"

Accurate velocity information is often essential to the control of robot manipulators, especially for precise tracking of fast trajectories. However, joint velocities are rarely directly measured and instead estimated to save costs. While many approaches have been proposed for the velocity estimation of robot joints, no comprehensive experimental evaluation exists, making it difficult to choose the appropriate method. This paper compares multiple estimation methods running on a six degrees-of-freedom manipulator. We evaluate: 1) the estimation error using a high-accuracy ground-truth signal, 2) the closed- loop tracking error, 3) convergence behavior, 4) sensor fault tolerance, 5) implementation and tuning effort. To ensure a fair comparison, we optimally tune the estimators using a genetic algorithm. All estimation methods have a similar estimation error and similar closed-loop tracking performance, except for the nonlinear high-gain observer, which is not accurate enough. Sliding-mode observers can provide a precise velocity estimation despite sensor faults. This dataset contains the implemented estimation methods, as well as the recorded data for the comparison.

精确的速度信息对于机器人操作器的控制至关重要，尤其是在精确追踪快速轨迹时。然而，关节速度很少直接测量，而是通过估算以节约成本。尽管已提出多种机器人关节速度估算方法，但尚无全面的实验评估，这使得选择合适的方法变得困难。本文对比了在六自由度操作器上运行的多种估算方法。我们评估了：1）使用高精度地面真实信号估算误差，2）闭环跟踪误差，3）收敛行为，4）传感器故障容错性，5）实现和调整努力。为确保比较的公正性，我们使用遗传算法对估算器进行最优调整。所有估算方法在估算误差和闭环跟踪性能方面相似，唯独非线性高增益观察者准确性不足。滑动模态观察者能够在传感器故障的情况下提供精确的速度估算。此数据集包含了实现的估算方法，以及用于比较的记录数据。