Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control
This addresses stability issues for quadruped robots in search and rescue or exploration tasks, but it is incremental as it applies an existing control method to a specific robot scenario.
The paper tackled the problem of instability in quadruped robots traversing challenging terrain by developing a Model Predictive Control (MPC) controller for terrain-adaptive gait transitioning, resulting in the robot correcting external perturbations up to 150 N and resisting falls up to 80 cm.
Legged robots can traverse challenging terrain, use perception to plan their safe foothold positions, and navigate the environment. Such unique mobility capabilities make these platforms a perfect candidate for scenarios such as search and rescue, inspection, and exploration tasks. While traversing through such terrains, the robot's instability is a significant concern. Many times the robot needs to switch gaits depending on its environment. Due to the complex dynamics of quadruped robots, classical PID control fails to provide high stability. Thus, there is a need for advanced control methods like the Model Predictive Control (MPC) which uses the system model and the nature of the terrain in order to predict the stable body pose of the robot. The controller also provides correction to any external disturbances that result in a change in the desired behavior of the robot. The MPC controller is designed in MATLAB, for full body torque control. The controller performance was verified on Boston Dynamics Spot in Webots simulator. The robot is able to provide correction for external perturbations up to 150 N and also resist falls till 80 cm.