Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots

TL;DR

This paper presents a lightweight, sensor-efficient Kalman filter-based estimator for hopping robots that accurately predicts hop height and trajectory using only inertial data, suitable for resource-constrained and degraded environments.

Contribution

A novel training procedure for a coupled hopping phase and Kalman filter estimator that requires only inertial measurements, improving robustness and efficiency in lightweight hopping robots.

Findings

Achieves 12.5% mean absolute percent error in hop height at 4m hops.

Operates at 840 Hz on a 240 MHz dual-core processor.

Effective even with sensor damage or occlusion.

Abstract

Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and significant computational power to achieve full state estimation. This, however, poses a significant challenge to the development of light-weight, high-performance, low observable, jamming and electronic interference resistant hopping systems; especially in perceptually degraded environments (e.g., dust, smoke). Here we show a training procedure for a coupled hopping phase and Kalman filter-based vertical state estimator, requiring only inertial measurements, which is able to learn the characteristics of the target system, sensors, locomotion behaviors, environment, and acceleration measurement aliasing conditions. The resulting estimator, given hop heights up to 4 m and velocities up to $\pm7$ m/s, achieves a mean absolute percent error in the hop apex height of 12.5% with an aerial trajectory average normalized mean absolute error in position and velocity of 19% and 16.5%, respectively; while operating at 840 Hz, on a dual-core 240 MHz processor, with a total robot mass of 672 g. Due to the low mass and computational power, the presented estimator could also be used as a degraded operational mode in cases of sensor damage, malfunction, or occlusion in more complex robots.