Path and footfall planning for N-legged and climbing robots — A model predictive control approach

In this paper, we present a general control framework for N-legged and variably-configured robots, designed to coordinate leg movements for climbing tasks without relying on Central Pattern Generators (CPGs). Model-based path and footfall planners are introduced to minimize actuator effort, minimize robot detachment risk, improve payload distribution between legs, and maximize the traveled distance during the swing phase. To achieve this, we address the force distribution problem (FDP) by selecting configurations where the robot is most comfortable in terms of kinematics, effort, and safety. A gait controller is presented as a nonperiodic, nonsymmetric, and nonregular bioinspired method that selects the most convenient leg to move by ensuring comfort, safety, and robot capabilities. The system has been tested in simulation with different robot configurations (varying number of legs and arrangements) and with the physical robot ROMERIN in its quadruped version.