Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learnin

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)

Searching a Target with a Mobile Robot

This paper presents an application of the Markov Decision Processes to search a target with a mobile robot. The robot does not know the absolute position of the target. It only knows the position of the target relative to its position. Markov Decision Processes have been widely used in mobile robotics. In this paper, we show experimentally that they are well suited for our task. Moreover, we explain how the mobile robot plans and re-plans a path to the target taking into account the incomplete knowledge it has on the target. Some simulation experiments are given

Steps towards safe navigation in open and dynamic environments

Abstract — Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Bassically, these problems can be classified into three main categories: SLAM in dynamic environments; Detection, characterization, and behavior prediction of the potential moving obstacles; On-line motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: Characterization and motion prediction of the observed moving entities using bayesian programming; Online goal-oriented navigation decisions using the Partial Motion Planning ( ) paradigm. A. Outline of the proble

Effect Of Site Location And Collecting Period On The Chemical Composition Of Hyptis Spicigera Lam. An Insecticidal Essential Oil From North-Cameroon

Hyptis spicigera essential oils from seven localities in the North-Cameroon (Ngaoundere, Guirvidig, Kodeck, Lara, Toloum, Kaele, Tchecal-baila) were investigated by GC and GC/MS. Results showed differences within harvesting sites and between the different sites of collection but did not revealed clear tendencies in the evolution of the oil composition with regard to the sampling period. The main group of compounds in all the analyzed samples were: alpha-pinene (11.9%-42.1%), beta-pinene + sabinene (6.0%-39.8%) and beta-phellandrene + 1,8-cineole(8.8%-27.4%) except in one oil where beta-caryophyllene (23.4%) was the principal component. The insecticidal activity of H. spicigera and its principal terpenic components was evaluated against the cowpea weevil Callosobruchus maculatus F., the major cause of damages of cowpea (Vigna unguiculata (L.) Walp) in North Cameroon