Differential-Drive Mobile Robot Control Using A Cloud Of Particles Approach

Common control systems for mobile robots include the use of some deterministic control law coupled with some pose estimation method, such as the extended Kalman filter, by considering the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. These methods are well suited to handle the uncertainty in pose estimation but demand significant processing power. In order to reduce the required processing power and still allow for multimodal or non-Gaussian uncertain distributions, we propose a scheme based on a particle filter and a corresponding cloud of control signals. The approach avoids the use of the certainty equivalence principle by postponing the decision on the optimal estimate to the control stage. As the mapping between the pose space and the control action space is nonlinear and the best estimation of robot pose is uncertain, postponing the decision to the control space makes it possible to select a better control action in the presence of multimodal and non-Gaussian uncertainty models. Simulation results are presented.