A genetic algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. The SLAM problem is defined as a global optimization problem in which the objective is to search the space of possible robot maps. A genetic algorithm is described for solving this problem, in which a population of candidate solutions is progressively refined in order to find a globally optimal solution. The fitness values in the genetic algorithm are obtained with a heuristic function that measures the consistency and compactness of the candidate maps. The results show that the maps obtained are very accurate, though the approach is computationally expensive. Directions for future research are also discussed

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Genomic organization and chromosomal localization of the murine 2 P domain potassium channel gene Kcnk8: conservation of gene structure in 2 P domain potassium channels.

A 2 P domain potassium channel expressed in eye, lung, and stomach, Kcnk8, has recently been identified. To initiate further biochemical and genetic studies of this channel, we assembled the murine Kcnk8 cDNA sequence, characterized the genomic structure of the Kcnk8 gene, determined its chromosomal localization, and analyzed its activity in a Xenopus laevis oocyte expression system. The composite cDNA has an open reading frame of 1029 bp and encodes a protein of 343 amino acids with a predicted molecular mass of 36 kDa. Structure analyses predict 2 P domains and four potential transmembrane helices with a potential single EF-hand motif and four potential SH3-binding motifs in the COOH-terminus. Cloning of the Kcnk8 chromosomal gene revealed that it is composed of three exons distributed over 4 kb of genomic DNA. Genome database searching revealed that one of the intron/exon boundaries identified in Kcnk8 is present in other mammalian 2 P domain potassium channels genes and many C. elegans 2P domain potassium channel genes, revealing evolutionary conservation of gene structure. Using fluorescence in situ hybridization, the murine Kcnk8 gene was mapped to chromosome 19, 2B, the locus of the murine dancer phenotype, and syntenic to 11q11-11q13, the location of the human homologue. No significant currents were generated in a Xenopus laevis oocyte expression system using the composite Kcnk8 cDNA sequence, suggesting, like many potassium channels, additional channel subunits, modulator substances, or cellular chaperones are required for channel function

Monocular Vision as a Range Sensor

One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robot’s field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360º view of the surrounding environment as part of map construction

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available