Robot localization in symmetric environment

The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. Monte Carlo Localization as a well known localization algorithm represents a robot\u27s belief by a set of weighted samples. This set of samples approximates the posterior probability of where the robot is located. Our method presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The sample sets in MCL often become impoverished when samples are generated in several locations. Our approach incorporates the idea of clustering the samples and organizes them considering to their orientation. Experimental results show our method is able to successfully determine the position of the robot in symmetric environment, while ordinary MCL often fails

Robust Global Localization Using Clustered Particle Filtering

Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabilistic algorithms known as Monte Carlo Localization (MCL) is currently among the most popular methods for solving this problem. MCL algorithms represent a robot's belief by a set of weighted samples, which approximate the posterior probability of where the robot is located by using a Bayesian formulation of the localization problem. This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The problem arises from the fact that sample sets in MCL often become impoverished, when samples are generated according to their posterior likelihood. Our approach incorporates the idea of clusters of samples and modifies the proposal distribution considering the probability mass of those clusters. Experimental results are presented that show that this new extension to the MCL algorithm successfully localizes in symmetric environments where ordinary MCL often fails.Comment: 6 pages. Proceedings of AAAI-2002 (in press

An Improved Clustering based Monte Carlo Localization approach for Cooperative Multi-robot Localization

This thesis describes an approach for cooperative multi-robot localization based on probabilistic method (Monte Carlo Localization) used in assistant robots which are capable of sensing and communicating one with another. In our approach, each of the robots maintains its own clustering based MCL algorithm, and communicates with each other whenever it detects another robot. We develop a new information exchange mechanism, which makes use of the information extracted from the clustering component, to synchronize the beliefs of detected robots. By avoiding unnecessary information exchange whenever detection occurs through a belief comparison, our approach can solve the delayed integration problem to improve the effectiveness and efficiency of multi-robot localization. This approach has been tested in both real and simulated environments. Compared with single robot localization, the experimental results demonstrate that our approach can notably improve the performance, especially when the environments are highly symmetric

An exact equilibrium reduced density matrix formulation I: The influence of noise, disorder, and temperature on localization in excitonic systems

An exact method to compute the entire equilibrium reduced density matrix for systems characterized by a system-bath Hamiltonian is presented. The approach is based upon a stochastic unraveling of the influence functional that appears in the imaginary time path integral formalism of quantum statistical mechanics. This method is then applied to study the effects of thermal noise, static disorder, and temperature on the coherence length in excitonic systems. As representative examples of biased and unbiased systems, attention is focused on the well-characterized light harvesting complexes of FMO and LH2, respectively. Due to the bias, FMO is completely localized in the site basis at low temperatures, whereas LH2 is completely delocalized. In the latter, the presence of static disorder leads to a plateau in the coherence length at low temperature that becomes increasingly pronounced with increasing strength of the disorder. The introduction of noise, however, precludes this effect. In biased systems, it is shown that the environment may increase the coherence length, but only decrease that of unbiased systems. Finally it is emphasized that for typical values of the environmental parameters in light harvesting systems, the system and bath are entangled at equilibrium in the single excitation manifold. That is, the density matrix cannot be described as a product state as is often assumed, even at room temperature. The reduced density matrix of LH2 is shown to be in precise agreement with the steady state limit of previous exact quantum dynamics calculations.Comment: 37 pages, 12 figures. To appear in Phys. Rev.