A Low-Cost, Highly Customizable Solution for Position Estimation in Modular Robots

TL;DR

This paper introduces a cost-effective, customizable position sensing method using PaintPots combined with a Kalman filter for modular robots, enabling accurate state estimation within tight volume constraints.

Contribution

It develops a simplified Kalman filter observation model and a comprehensive calibration and characterization process for PaintPots in modular robots.

Findings

Effective position estimation in modular robots using PaintPots.

Cost reduction compared to traditional sensors.

Adaptable solution for various sensing modalities.

Abstract

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.