Accurate position sensing is important for state estimation and control in robotics. Reliable and accurate position sensors are usually expensive and difficult to customize. Incorporating them into systems that have very tight volume constraints such as modular robots are particularly difficult. PaintPots are low-cost, reliable, and highly customizable position sensors, but their performance is highly dependent on the manufacturing and calibration process. This paper presents a Kalman filter with a simplified observation model developed to deal with the non-linearity issues that result in the use of low-cost microcontrollers. In addition, a complete solution for the use of PaintPots in a variety of sensing modalities including manufacturing, characterization, and estimation is presented for an example modular robot, SMORES-EP. This solution can be easily adapted to a wide range of applications.
翻译:准确位置感测对于国家估计和控制机器人十分重要。 可靠和准确位置传感器通常费用昂贵,难以定制。 将它们纳入数量限制非常紧的系统,如模块机器人,难度特别大。 油漆点是低成本、可靠和可定制的高度位置感测器,但其性能在很大程度上取决于制造和校准过程。 本文介绍了卡尔曼过滤器,该过滤器有一个简化的观测模型,用于处理导致使用低成本微控制器的非线性问题。 此外,还介绍了在包括制造、定性和估计在内的多种遥感模式中使用油漆点的完整解决方案,例如模块机器人SMORES-EP。 这一解决方案可以很容易地适应广泛的应用。