Sonar-based Mapping Research Articles

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods – generalized additive models (GAM) and random forest (RF) – together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Occupancy grid mapping with the use of a forward sonar model by gradient descent

Mapping of the environment is one of the key functions of autonomous robots. An efficient mapping algorithm is needed for successful localization and path finding. Maps may be plotted on the basis of the data from various sensors. Cheap and easy-to-install sonars are typically used for this purpose. A sonar-based mapping algorithm based on continuous optimization methods is proposed. This algorithm may be used in real time, and its main advantages over the common methods are more accurate determination of the size of small (smaller than the sonar beam width or comparable to it) obstacles and more reliable detection of narrow passages (e.g., doorways).

Navigation Method for a Mobile Robot via Sonar-based Crop Row Mapping and Fuzzy Logic Control

A navigation method, which employs sonar-based mapping of crop rows and fuzzy logic control-based steering, is described for a wheeled mobile robot in an agricultural environment. Crop rows are exploited for automatic navigation of a mobile robot without the need to construct artificial landmarks. The crop row map is constructed from the sonar readings and is transferred to the fuzzy logic control system, which steers the robot along the crop row. Three experiments concerning crop row mapping, fuzzy logic control, and their combination, were conducted using the experimental robot in a simulated maizefield. The errors found in evaluating the performance of these techniques were as follows: the position and directional standard errors in the mapping tests were 12·7 mm and 2·4°; those in the steering tests 16·3 mm and 2·2°; and in the overall tests 33·6 mm and 3·2°.

Sonar-based real-world mapping and navigation

A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

A distributed control architecture for an autonomous mobile robot

This paper describes a Distributed Control Architecture for an autonomous mobile robot. We start by characterizing the Conceptual Levels into which the various problem-solving activities of a mobile robot can be classified. In sequence, we discuss a Distributed Control System that provides scheduling and coordination of multiple concurrent activities on a mobile robot. Multiple Expert Modules are responsible for the various tasks and communicate through messages and over a Blackboard. As a testbed, the architecture of a specific system for Sonar-Based Mapping and Navigation is presented, and a distributed implementation is described.