Spring-legged quadcopter cuts power use by nearly half hopping
Learning to Exploit Passive Dynamics for Energy-Efficient Target Hopping of a Spring-Legged Quadcopter
Robotics
Summary
Hopping robots that combine flying with springy legs can move well over rough ground, but getting their motors to work smoothly with their springs is tricky. The authors taught a robot’s four motors to control hopping without a complicated step-by-step program by using a learning method called PPO. This new approach used less electricity, managed energy better, and helped the robot land more precisely compared to older methods. It shows machines can learn to save power by better using their natural springs.
What this means in practice
- •For robotics engineers: Design energy-efficient hopping robots that require less tuning and achieve better power use by combining learned motor control with passive leg dynamics.
- •For drone developers: Build quadcopter drones capable of agile hopping over complex terrain while reducing battery consumption through learned control strategies.
Authors
Ruigang Chen, Qi Zhang, Zhicheng Zhong, Zhuorui Yun, Yizhar Or, Mingyi Liu
Abstract
Combining aerial thrust with spring-loaded hopping makes monopedal quadcopters promising for locomotion over complex terrain, but heuristic proportional-integral-derivative (PID) tuning limits coordination between active thrust and passive contact dynamics. We present a direct estimated-state-to-motor Proximal Policy Optimization (PPO) policy that commands four motors without an explicit hopping state machine or low-level attitude PID. Its reward combines Energy-Manifold Shaping for mass-normalized vertical-energy tracking and apex-state anchoring with Efficiency Shaping, which uses a history-aware power estimator to penalize general power use, impose an additional airborne-power cost, and penalize airborne near-stationarity. In representative hardware runs, the PPO-based control stack reduced cycle-averaged measured electrical power by 30.7% and mean total normalized thrust by 49.8% relative to the tuned PID-based control stack, while retaining repeatable commanded-height hopping and more concentrated landings. These observations are consistent with improved use of passive dynamics and reduced measured electrical demand.