Abstract
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.
Highlights
Mobile robots are known to be subject to uncertainty in both the robot behavior and the environment where the robot navigates
The current state-of-the-art approach to cope with uncertainties, especially those with non-Gaussian probability distributions, is to use Bayesian filters to estimate the system state and compute a control signal based on the result of the estimation procedure, which is a probability density, a histogram, or a set of particles or probabilities over a topological map
This signal can be obtained from a mode or through optimization, such as partially observable Markov decision processes (POMDP) approaches.[4,5]
Summary
Mobile robots are known to be subject to uncertainty in both the robot behavior and the environment where the robot navigates. This model will be used for estimating the state transitions for a set of possible values for the state vector, as explained in detail in section ‘‘Pose estimation by particle filter.’’ It is important to note that even though wtðkÞ, wd[1] ðkÞ, and wd[2] ðkÞ are assumed to be Gaussian, the resulting state xðk þ 1Þ is not Gaussian, due to nonlinearities.
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