Model predictive obstacle avoidance for a leg/wheel mobile robot utilizing sample-based optimization

Leg/wheel mobile robots are expected to play an active role in environments with many obstacles because their leg and wheel mechanisms allow them to adapt to uneven terrain and move efficiently. In this study, for a planar leg/wheel mobile robot, we develop an obstacle avoidance control that combines Model Predictive Control (MPC) based on Markov Chain Monte Carlo (MCMC), a sample-based solution method, and MPC based on a numerical solution to the Euler-Lagrange equations. Specifically, the optimal input is calculated utilizing both MCMC samples and the C/GMRES, and then the samples for the next control cycle are generated through resampling. This approach generates a sub-optimal control input sequence while searching for a global optimal solution, which anticipates that the robot prevents from stacking into a local optimum. The effectiveness of the proposed method is confirmed by comparing it with the MCMPC or C/GMRES methods, respectively, and implementing it into the onboard computer equipped with the actual robot.