Quadruped robots have proved their robustness to cross complex terrain despite little environment knowledge. Yet advanced locomotion controllers are expected to take advantage of exteroceptive information.
The paper presents a complete method to plan and control the locomotion of quadruped robots when 3D information about the surrounding obstacles is available, based on several stages of decision.
This paper presents a complete method to plan and control the locomotion of quadruped robots when 3D information about the surrounding obstacles is available, based on several stages of decision.
We first propose a contact planner formulated as a mixed-integer program, optimized on-line at each new robot step. It selects a surface from a set of convex surfaces describing the environment for the next footsteps while ensuring kinematic constraints.
We then propose to optimize the exact contact location and the feet trajectories at control frequency to avoid obstacles, thanks to an efficient formulation as quadratic programs optimizing Bezier curves.
By relying on the locomotion controller of our quadruped robot Solo, we finally implement the complete method, provided as an open-source package.
Its efficiency is asserted by statistical evaluation of the importance of each components in simulation, while the overall performances are demonstrated on various scenarios with the real robot.
We then propose to optimize the exact contact location and the feet trajectories at control frequency to avoid obstacles, thanks to an efficient formulation of quadratic programs optimizing Bezier curves.
By relying on the locomotion controller of our quadruped robot Solo, we finally implement the complete method, provided as an open-source package.
Its efficiency is asserted by statistical evaluation of the importance of each component in simulation, while the overall performances are demonstrated on various scenarios with the real robot.
