Tovar, Benjamin
Overview
Works:  4 works in 4 publications in 1 language and 18 library holdings 

Genres:  Academic theses 
Roles:  Author 
Publication Timeline
.
Most widely held works by
Benjamin Tovar
Extracting visibility information by following walls(
)
1 edition published in 2007 in English and held by 15 WorldCat member libraries worldwide
1 edition published in 2007 in English and held by 15 WorldCat member libraries worldwide
Learning Combinatorial Map Information from Permutations of Landmarks(
Book
)
1 edition published in 2010 in English and held by 1 WorldCat member library worldwide
This paper considers a robot that moves in the plane and is only able to sense the cyclic order of landmarks with respect to its current position. No metric information is available regarding the robot or landmark positions; moreover, the robot does not have a compass or odometers (i.e., knowledge of coordinates). We carefully study the information space of the robot, and establish its capabilities in terms of mapping the environment and accomplishing tasks, such as navigation and patrolling. When the robot moves exclusively inside the convex hull of the set of landmarks, the information space can be succinctly characterized as an order type, which provides information powerful enough to determine which points lie inside the convex hulls of subsets of landmarks. Additionally, if the robot is allowed to move outside the convex hull of the set of landmarks, the information space is described with a swap cell decomposition, which is an aspect graph in which each aspect is a cyclic permutation of landmarks. We show how to construct such decomposition through its dual, using two kinds of feedback motion commands based on the landmarks sensed
1 edition published in 2010 in English and held by 1 WorldCat member library worldwide
This paper considers a robot that moves in the plane and is only able to sense the cyclic order of landmarks with respect to its current position. No metric information is available regarding the robot or landmark positions; moreover, the robot does not have a compass or odometers (i.e., knowledge of coordinates). We carefully study the information space of the robot, and establish its capabilities in terms of mapping the environment and accomplishing tasks, such as navigation and patrolling. When the robot moves exclusively inside the convex hull of the set of landmarks, the information space can be succinctly characterized as an order type, which provides information powerful enough to determine which points lie inside the convex hulls of subsets of landmarks. Additionally, if the robot is allowed to move outside the convex hull of the set of landmarks, the information space is described with a swap cell decomposition, which is an aspect graph in which each aspect is a cyclic permutation of landmarks. We show how to construct such decomposition through its dual, using two kinds of feedback motion commands based on the landmarks sensed
Minimalist models and methods for visibilitybased tasks by Benjamin Tovar(
)
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
This dissertation proposes minimal models for solving visibilitybased robotic tasks. It introduces strategies that handle sensing and actuation uncertainty while avoiding precise state estimations. This is done by analyzing the space of sensing and actuation histories, the history information space. The history information space is compressed into smaller spaces, called derived information spaces, which are used for filtering and planning. By designing and analyzing the derived information spaces, we determine minimal information requirements to solve the robotic tasks. In this context, minimal information refers to the detection combinatorial properties of the environment necessary to complete the task. Examples of these combinatorial properties are the order type of a configuration of landmarks, or the inflection arrangement of a polygonal boundary. By establishing that certain tasks can be solved using simple sensors that detect these properties, formal performance guarantees are made while avoiding substantial modeling challenges. From this perspective, the thesis provides novel strategies for classical robotic tasks, such as navigation in unknown planar environments, navigation among unknown sets of landmarks, and visibilitybased pursuitevasion. Information is recovered from combinatorial events with models of sensors unable to gather metric information (e.g., distances or angles), or global reference frames (e.g., without a compass, or a global positioning system). These combinatorial events served as the base of a sensor beam abstraction, from which several inferences about the path followed by the robot are made
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
This dissertation proposes minimal models for solving visibilitybased robotic tasks. It introduces strategies that handle sensing and actuation uncertainty while avoiding precise state estimations. This is done by analyzing the space of sensing and actuation histories, the history information space. The history information space is compressed into smaller spaces, called derived information spaces, which are used for filtering and planning. By designing and analyzing the derived information spaces, we determine minimal information requirements to solve the robotic tasks. In this context, minimal information refers to the detection combinatorial properties of the environment necessary to complete the task. Examples of these combinatorial properties are the order type of a configuration of landmarks, or the inflection arrangement of a polygonal boundary. By establishing that certain tasks can be solved using simple sensors that detect these properties, formal performance guarantees are made while avoiding substantial modeling challenges. From this perspective, the thesis provides novel strategies for classical robotic tasks, such as navigation in unknown planar environments, navigation among unknown sets of landmarks, and visibilitybased pursuitevasion. Information is recovered from combinatorial events with models of sensors unable to gather metric information (e.g., distances or angles), or global reference frames (e.g., without a compass, or a global positioning system). These combinatorial events served as the base of a sensor beam abstraction, from which several inferences about the path followed by the robot are made
Optimal navigation without geometric maps by Benjamín Tovar(
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Audience Level
0 

1  
Kids  General  Special 
Languages