Online Dynamic Trajectory Optimization and Control for a Quadruped Robot

TL;DR

This paper presents an online trajectory optimization framework for quadruped robots that generates stable, adaptable trajectories in real-time for uneven terrain, obstacles, and environmental changes, demonstrated through simulation and real-world tests.

Contribution

The novel framework enables fast, online generation of stable base and footstep trajectories for quadruped robots, adaptable to various scenarios including moving obstacles and uneven terrain.

Findings

Successfully tested on real robot with uneven terrain and obstacles

Capable of real-time replanning to accommodate environmental changes

Generated stable trajectories even with last-moment obstacle appearance

Abstract

Legged robot locomotion requires the planning of stable reference trajectories, especially while traversing uneven terrain. The proposed trajectory optimization framework is capable of generating dynamically stable base and footstep trajectories for multiple steps. The locomotion task can be defined with contact locations, base motion or both, making the algorithm suitable for multiple scenarios (e.g., presence of moving obstacles). The planner uses a simplified momentum-based task space model for the robot dynamics, allowing computation times that are fast enough for online replanning.This fast planning capabilitiy also enables the quadruped to accommodate for drift and environmental changes. The algorithm is tested on simulation and a real robot across multiple scenarios, which includes uneven terrain, stairs and moving obstacles. The results show that the planner is capable of generating stable trajectories in the real robot even when a box of 15 cm height is placed in front of its path at the last moment.