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Adaptive navigation of scalar fields is a compelling capability in which mobile robotic systems make real-time navigation decisions based on sensed measurements of the environment. This capability can enable efficient identification and location of specific features of interest within the field of interest, potentially saving time and energy while also being responsive to changing conditions. Applications can include finding the sources and impact zones of a pollutant, establishing hazard perimeters, finding safe zones, and safe paths of travel. This paper presents new work that experimentally verifies several adaptive navigation control policies for moving to/along critical scalar field features with a group of mobile robots. Specifically, we demonstrate the use of a five robot cluster of sensor-equipped mobile robots to descend ridges within a scalar field, to ascend trenches, and to move to and hold a position at saddle points. This is done through the use of differential measurements across the cluster's formation baselines and control laws that have been previously demonstrated in simulation. This paper also incorporates a new state machine within the adaptive navigation control architecture in order to monitor the performance of the individual control primitives and to respond to conditions such as losing track of the feature of interest. Finally, this paper is the first in which we have experimentally demonstrated control of a five robot group of robots using our cluster space control methodology. The experiments were conducted using a novel indoor multi-robot testbed with the ability to establish customizable scalar fields printed in greyscale on large sheets that are actively sensed by the robots to enable controlled experimental evaluation. Four different fields are used in this study in order to demonstrate the new capabilities of interest.

Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of the environment. Compared to conventional navigation methods, these techniques can reduce required time and energy to explore scalar characteristics of unknown and dynamic regions of interest (e.g., temperature, concentration level). Multiple Uncrewed Aerial Vehicle (UAV) approaches to AN can improve performance by exploiting synchronized spatially-dispersed measurements to generate realtime information regarding the structure of the local scalar field, which is then used to inform navigation decisions. This article presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using our unique multilayer control architecture for groups of vehicles. Using our flexible formation control system, we build upon our prior 2D AN work and provide new contributions to 3D scalar field AN by a) demonstrating a wide range of 3D AN capabilities using a unified, multilayer control architecture, b) extending multivehicle 2D AN control primitives to navigation in 3D scalar fields, and c) introducing statebased sequencing of these primitive AN functions to execute 3D mission-level capabilities such as isosurface mapping and plume following. We verify functionality using high-fidelity simulations of multicopter drone clusters, accounting for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise. This paper presents the multilayer architecture for multivehicle formation control, the 3D AN control primitives, the sequencing approaches for specific mission-level capabilities, and simulation results that demonstrate these functions.