Autonomous river navigation

Snyder, Franklin D.; Morris, Daniel; Haley, Paul; Collins, Robert T.; Okerholm, Andrea M.

doi:10.1117/12.580579

Cited by 27 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The problem described here is the opposite to a convex hull, as a perimeter that contains no points (links) is required. This problem can be solved using the inverse convex hull algorithm [15], which selects a set of points that forms a shape that contains no points. The inverse convex hull algorithm first inverts the distance of discrete points from an origin, following which the convex hull of that set is found.…”

Section: Inverse Convex Hullmentioning

confidence: 99%

Biologically Inspired Perimeter Detection for Whole-Arm Grasping

et al. 2013

View full text Add to dashboard Cite

Grasping is a useful ability that allows manipulators to constrain objects to a desired location or trajectory. Whole-arm grasping is a specific method of grasping an object that uses the entire surface of the manipulator to apply contact forces. Elephant trunks and snakes and octopus arms are illustrative of these methods. One of the greatest challenges of whole-arm grasping in poorly defined environments is accurately identifying the perimeter of an object. Existing algorithms for this task use restrictive assumptions or place unrealistic demands on the required hardware. Here, a new algorithm (termed Octograsp) has been developed as a method of gaining information on the shape of the grasped object through tactile information alone. The contact information is processed using an inverse convex hull algorithm to build a model of the object's shape and position. The performance of the algorithm is examined using both simulated and experimental hardware. Methods of increasing the level of contact information through repeated contact attempts are presented. It is demonstrated that experimentally obtained, coarsely spaced, contact information can result in an accurate model of an object's shape and position.

show abstract

Section: Inverse Convex Hullmentioning

confidence: 99%

Biologically Inspired Perimeter Detection for Whole-Arm Grasping

et al. 2013

View full text Add to dashboard Cite

show abstract

“…ASV literature that specifically deals with the obstacle avoidance problem focuses on obstacle detection for above-surface obstacles. Larson et al [10] performed ASV navigation using vision and radar, Bandyophadyay et al [11] used a laser range finder to perform obstacle avoidance in the Port of Singapore and Snyder et al [12] performed autonomous navigation in rivers using a camera system. Benjamin [16].…”

Section: Prior Workmentioning

confidence: 99%

Obstacle detection from overhead imagery using self-supervised learning for Autonomous Surface Vehicles

Heidarsson

Sukhatme

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-We describe a technique for an Autonomous Surface Vehicle (ASV) to learn an obstacle map by classifying overhead imagery. Classification labels are supplied by a front-facing sonar, mounted under the water line on the ASV. We use aerial imagery from two online sources for each of two water bodies (a small lake and a harbor) and train classifiers using features generated from each image source separately, followed by combining their output. Data collected using a sonar mounted on the ASV were used to generate the labels in the experimental study. The results show that we are able to generate accurate obstacle maps wellsuited for ASV navigation.

show abstract

“…The group currently includes researches on automation of water vessels sailing along a track, or a coastline on a given light sector and other navigational systems integrating appliances [Hoshizaki et al, 2004;Snyder et al 2004;Ryynanen et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

Determining Ship Position In A Harbour Based On Omnidirectional Image Of The Coastline

Naus

Wąż

2014

Annual of Navigation

View full text Add to dashboard Cite

The following article presents researches aimed at the evaluation of precision in determining a ship's position through comparing a omnidirectional map image to a real vision image of the coast line. The first part establishes the thesis and preparatory forms in conducting the research. It also presents designed and built equipment including a research tool software. A system equipped with a spherical catadioptric camera that aids data collection on board ship designated to processing and analyzing data collected on board in connection with the spherical images of an electronic navigational chart with a software module. The second part explains procedures followed in conducting the research. The foreword note explains the procedure in data collection aboard a ship maneuvering in the port after which the algorithm for position placement and precise parametrical count was presented. The concluding part shows analyses of obtained research result. It bears a performance on the evaluation of precision at determining position. As a measure, an average error value and distance fluctuation of obtained position from referential position. As our conclusion, primary agents having rudimentary influence on the quality of correlating spherical map image to coastline visual image were characterized.

show abstract

Autonomous river navigation

Cited by 27 publications

References 7 publications

Biologically Inspired Perimeter Detection for Whole-Arm Grasping

Biologically Inspired Perimeter Detection for Whole-Arm Grasping

Obstacle detection from overhead imagery using self-supervised learning for Autonomous Surface Vehicles

Determining Ship Position In A Harbour Based On Omnidirectional Image Of The Coastline

Contact Info

Product

Resources

About