Monte Carlo tree search gait planning with model predictive control for non-gaited legged system controller

Abstract

In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-making process. The redefined contact sequence problem is solved by utilizing a Monte Carlo Tree Search (MCTS) algorithm that exploits optimization-based simulations to evaluate the best search direction. The proposed scheme has proven to be considerably less computationally demanding than the state of the art Mixed-Integer Quadratic Programming (MIQP) solver without drastically compromise the solution performance. The gait generated by the MCTS is utilized as input in a model predictive control (MPC) scheme for the ground reaction forces and future footholds position optimization. The simulation results, performed on a quadruped robot, shown that the proposed framework is able to generate known periodic gait as well as adapting the contact sequence to the encountered conditions. The system has also shown its reliability when tested on robots with different limbs layout as well as on terrain with unknown and variable properties.