AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models. A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).