This paper presents a natural framework for information sharing in cooperative tasks involving humans and robots. In this framework, all information gathered over time by a human-robot team is exchanged and summarized in the form of a fused probability density function (pdf). An approach for an intelligent system to describe its belief pdfs in English expressions is presented. This belief expression generation is achieved through two goodness measures: semantic correctness and information preservation. In order to describe complex, multimodal belief pdfs, a Mixture of Statements (MoS) model is proposed such that optimal expressions can be generated through compositions of multiple statements. The model is further extended to a nonparametric Dirichlet process MoS generation, such that the optimal number of statements required for describing a given pdf is automatically determined. Results based on information loss, human collaborative task performances, and correctness rating scores suggest that the proposed method for generating belief expressions is an effective approach for communicating probabilistic information between robots and humans.

This paper presents a framework for information sharing and fusion in cooperative tasks involving humans and robots. In this context, all information regarding the state of interest is recursively fused and maintained by each agent in a form of belief. For a robot agent, its belief is commonly and practically represented as a probability density function (pdf), formed by traditional sensor fusion and state estimation algorithms. In cooperative tasks with non-expert humans, a robot needs to effectively communicate its belief so that the gathered information can be easily processed and interpreted by the humans. The goal of this research is to provide two-way information exchange and fusion between robots and humans, the former operating on pdfs, while the latter on English sentences. This is achieved by considering two goodness measures: semantic correctness and information preservation. Based on the goodness measures studied, results show that the proposed framework is able to generate optimal statements describing the given belief pdfs and successfully recover the initial inputs used to generate them. Additionally, in order to describe complex belief pdfs, a Mixture of Statements (MoS) model is proposed such that the optimal expression can be generated through a composition of more than one statements. With a nonparametric Dirichlet Process MoS generation, it is found that the robot can determine correctly the number of statements as well as the corresponding reference parameters needed to describe all hypotheses underlying its belief.

A terrain mapping model is proposed using a generalized Markov random field (MRF) representation. Unlike previous work, the proposed MRF can fully represent uncertainties due to sensor pose and measurement errors, as well as data association errors in a single model. Additionally, neither homoscedasticity nor a predefined shape of the likelihood distribution is assumed. The flexibility of an MRF model allows spatial height correlations to be incorporated. The ability to include spatial correlations not only improves the accuracy through the benefits of Bayesian prior modeling, but also serves as a basis for terrain property characterization. Maximum likelihood solutions of terrain roughness are derived. Benefits of the proposed model are demonstrated experimentally on indoor and outdoor datasets. Results show that the MRF model leads to lower height estimation errors. In addition, the capability of estimating non-Gaussian height distributions allows the information about individual terrain features to be preserved. Finally, the model is able to accurately estimate the roughness of the terrain, which is beneficial for edge detection of obstacles and nontraversible terrain regions.

This paper presents an implementation of place cells for a robot navigation using the K-iterations fast learning artificial neural networks (KFLANN) clustering algorithm. The KFLANN possesses several desirable properties suitable for place cell robot navigation tasks. The technique proposed is able to autonomously adjust the resolution of cells according to the complexity of the environment. This is achieved through two parameters known as the tolerance and the vigilance of the network. In addition, a navigation system consisting of a topological map building and a place cell path planning strategy is presented. A physical implementation of the system was developed on an autonomous platform and actual results were obtained. The experimental results obtained indicate that the system was able to navigate successfully through the experimental space and also tolerate unexpected discrepancies arising from motor and sensor errors present in a real environment. Furthermore, despite abrupt changes in an environment due to the deliberate introduction of obstacles, the system was still able to cope without changes to the program. The experiment was also extended to include a kidnapped robot scenario and the results were favorable, indicating a positive use of allothetic cue recognition capabilities.