A novel method for optimal path synthesis of mechanisms based on tracking control of shadow robot

We consider navigation for a polygonal, holonomic robot in an obstacle filled environment in SE(2). We denote the configuration space of the robot as C. In order to determine the free space, obstacles are represented as point clouds then transformed into C. The point-wise Minkowski sum of the robot and obstacle points is then calculated in C by adding the vertices and points on the convex hull of robot to obstacle points for different robot configurations. We then find a seed path using either a graph search or sample based planner. This seed path is then used in our novel method to determine overlapping convex regions for each consecutive chord of the seed path. Our proposed method represents the collision free, traversable region by defining overlapping convex corridors defined by a set of linear constraints. Within these corridors we find feasible trajectories that optimize a specified cost functional. The generated corridors along with the initial and desired poses are then used to determine an optimal path that satisfies the specified objective within the same homotopy group as the seed path. The key contributions is the proposed methods’ ability to easily generate a set of convex, overlapping polytopes that effectively represent the traversable free space. This in turn lends itself to (a) efficient computation of optimal paths, and (b) extending these basic ideas to non-Euclidean spaces such as SE(2). We provide simulated examples and implement this algorithm on the KUKA youBot omni-directional base.

Download Full-text

THEA: A novel approach to gene identification in phage genomes

10.1101/265983 ◽

2018 ◽

Cited By ~ 3

Author(s):

Katelyn McNair ◽

Carol Zhou ◽

Brian Souza ◽

Robert A. Edwards

Keyword(s):

Gene Prediction ◽

Optimal Path ◽

Genome Structure ◽

Weighted Graph ◽

Open Reading Frames ◽

Functional Protein ◽

Protein Database ◽

Protein Coding ◽

Novel Approach ◽

Novel Method

AbstractMotivationCurrently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap, and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present THEA (The Algorithm), a novel method for gene calling specifically designed for phage genomes. While the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use graph theory to find the optimal path.ResultsWe compare THEA to other gene callers by annotating a set of 2,133 complete phage genomes from GenBank, using THEA and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with THEA predicting significantly more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and sequence read archive, and found that they are present at levels that suggest that these are functional protein coding genes.Availability and ImplementationThe source code and all files can be found at: https://github.com/deprekate/THEAContactKatelyn McNair: [email protected]

Download Full-text