Online Dynamic Trajectory Optimization and Control for a Quadruped Robot
This work addresses the challenge of real-time, stable motion planning for quadruped robots in dynamic environments, representing an incremental improvement in robotic locomotion control.
The researchers tackled the problem of generating stable locomotion trajectories for legged robots on uneven terrain by developing an online dynamic trajectory optimization framework, which successfully enabled a quadruped robot to handle obstacles like a 15 cm high box placed in its path at the last moment.
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.