Thruster-assisted bipedal robot performs bird-inspired trotting
Bio-inspired Thrust-Assisted Trotting on a legged-Aerial Robot via Real-Time QP-MPC
Robotics
Summary
Walking robots often struggle to move quickly and smoothly, especially on two legs. The authors built a robot called Harpy that uses small thrusters, like little rocket engines, to help it trot better. Inspired by how some birds use their wings to push off the ground, the robot's controller plans its moves very quickly to balance leg forces and thruster pushes. Their tests show the robot can follow target speeds while keeping its joints moving accurately, all while running its control calculations fast enough to react in real time.
What this means in practice
- •For robotics engineers: Build bipedal robots that use thrusters to improve walking stability and speed through real-time force and thrust control.
- •For drone developers: Incorporate thruster-assisted legged motion concepts to enhance multi-modal terrain mobility in aerial-ground hybrid drones.
Authors
Shreyansh Pitroda, Eric Sihite, Alireza Ramezani, Morteza Gharib
Abstract
This paper presents the hardware implementation of a quadratic program-based controller on Harpy: thruster-assisted bipedal robot to perform thruster-assisted trotting. This is inspired by wing-assisted locomotion observed in birds such as the chukar partridge, which employ thrust vectoring to manipulate the ground reaction force. The proposed controller formulates the thruster-assisted spring-loaded inverted pendulum dynamics as a condensed QP-based model predictive control problem, enabling simultaneous regulation of ground contact force and active thrust vectoring at 500 Hz. A decoupled control architecture is used, where the robot's sagittal dynamics are controlled using QP-MPC while the frontal dynamics is regulated using a cascade orientation stabilization controller. Two experiments were conducted with different forward reference velocities of 0 m/s and 0.1 m/s. While the instantaneous velocity oscillates, the mean velocities can track their respective reference. Additionally, joint tracking error remains within 1 deg while tracking the desired leg length given by the leg trajectory controller and desired swing trajectory given by Raibert's heuristic. The QP-MPC problem is consistently solved with 0.18ms, which is well within the 2ms computation budget while satisfying the GRF friction cone constraint.