Mobile Robot Control Using a Cloud of Particles

Common control systems for mobile robots include the use of deterministic control laws together with state estimation approaches and the consideration of the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. In order to reduce the required processing power and yet allow for multimodal or non-Gaussian distributions, a scheme based on a particle filter and a corresponding cloud of input signals is proposed in this paper. Results are presented and compared to a scheme with extended Kalman filter and the assumption that the certainty equivalence holds.