Scientists Crack Framework to Control Leg Movement of MicroRobots

New framework estimates and controls leg trajectories of a tiny four-legged robot.
Jessica Miley

Animals that live and move primarily on land navigate over their natural terrains using a set of complex leg trajectories. 

Individual animal leg trajectories are influenced by a variety of factors including the animal's posture, vision of leg and hip as well as ankle and shape of foot and actuation capabilities. 

For robotic engineers mimicking these movements is often extremely difficult as there are so many possibilities. The task just got a little easier thanks to a team of researchers from Harvard University and the Wyss Institute for Biologically Inspired Engineering who have developed a computationally efficient framework for the estimation and control of leg trajectories on a quadrupedal microrobot. 

Scientists' work to better mimic animals

They have pre-published their findings on arXiv. The paper details how they achieved accurate position estimation and control, as the robot moved across a wide range of stride frequencies (10-50Hz).

"Animals also modify their leg trajectories to meet performance requirements such as speed, stability, and economy, as well as to adapt to external factors such as terrain type and surface properties," the researchers wrote in their paper. 

"Inspired by their biological counterparts, large (body length ~100cm) bipedal and quadrupedal robots typically have two or more actuated degrees of freedom (DOF) per leg to enable complex leg trajectories." 

In the past, most small-legged robots could only achieve forward movement through carefully tuned mechanical leg trajectories. However, the recent improvements in manufacturing have enabled multi stride frequencies in tiny-legged robots.

A variety of control schemes are employed for the small-legged robots to achieve adjustable locomotion over varying terrains; these include optimization algorithms, controllers using stochastic kinematic models and deep reinforcement learning algorithms. 

HAMR robot perfect base for the locomotive hypothesis

While there have been many successful robots built with these techniques, they each come with their own set of limitations. The new research set out to improve upon the Harvard Ambulatory MicroRobot (HAMR), which uses high-bandwidth piezoelectric bending actuators so that it can achieve effective locomotion at multiple stride frequencies.

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"In this work, we leverage concomitant sensing for piezoelectric actuation to develop a computationally efficient framework for estimation and control of leg trajectories on a quadrupedal microrobot," the researchers wrote in their paper.

"We demonstrate accurate position estimation (<16% root-mean-square error) and control (<16% root-mean-square tracking error) during locomotion across a wide range of stride frequencies (10-50 Hz)." 

The HAMR robot is 4.5 cm long with four legs, it weighs in at 1.4g. Each of its legs has two DOFs, which are driven by piezoelectric bending actuators controlled with AC voltage signals. The new approach estimates the robots leg positions and velocity, then generates a variety of leg trajectories for improved locomotion based on these estimates. 

"In the future, we aim to use this low-level controller in conjunction with trajectory optimization to design feasible leg trajectories that optimize a given cost (e.g. speed, COT, etc.) at a particular operating condition," the researchers wrote.

"This can automate the challenging task of designing appropriate leg trajectories for a complex legged system and result in better locomotion performance."

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