Document Type
Article
Publication Date
3-22-2018
Publisher
IEEE
Abstract
Adaptive navigation is the process of modifying a vehicle's direction or motion path based on measurements taken while moving. When exploring a scalar field, such as the temperature or the concentration level of a pollutant across a region of interest, adaptive navigation may allow the identification of locations of interest-like the maximum temperature or the source of the pollutant-without exhaustively mapping the entire region. Adaptive navigation has been hailed as a powerful capability, and significant work has been performed to explore how such techniques can be used to find the local extreme points and follow contour levels in a field. Our own prior work in this field has matured to experimentally verifying and validating such capabilities through field demonstrations. Beyond extrema-finding and contour following, however, little to no prior work has been performed on moving to/along other critical features in a scalar field, such as down ridges, up trenches, and to saddle points; performing such maneuvers can be valuable for a number of applications. In this paper, we provide and verify via simulation new multirobot adaptive navigation controllers for moving with respect to these new features. We also present a multilayered control architecture that unifies the execution of all of our multirobot adaptive navigation control primitives: extrema finding, contour following, ridge/trench following, and saddle point positioning. In addition, we review several considerations related to the performance of these controllers within unknown scalar fields. Finally, we review ongoing and future work to experimentally verify our new controllers, improve and extend their performance, and apply them to real field applications.
Recommended Citation
Kitts, C. A., McDonald, R. T., & Neumann, M. A. (2018). Adaptive Navigation Control Primitives for Multirobot Clusters: Extrema Finding, Contour Following, Ridge/Trench Following, and Saddle Point Station Keeping. IEEE Access, 6, 17625–17642. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2818622
Comments
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