Mission-Oriented Multirobot Adaptive Navigation of Scalar Fields

Scalar fields are spatial regions where each point has an associated physical value. These fields often contain features of interest, such as local extrema and contours with a value of significance. Traditional navigation techniques require robots to exhaustively search these regions to find the areas of significance, while adaptive navigation allows them to move directly to the points of interest based on measurements of the field taken during the navigation process. This work expands existing adaptive navigation techniques by adding a finite state machine layer to the control architecture, and using it as a discrete mode controller; the state machine allows for the sequencing of individual adaptive navigation control primitives for the purpose of enhancing performance and achieving new mission-level capabilities. For example, it has enabled improvements to existing ridge, trench, and saddle point navigators and the creation of a novel technique for navigating along scalar fronts. In both cases, experimental results demonstrated excellent tracking of the features of interest. Furthermore, mission-level capabilities were developed for low-exposure waypoint navigation and mapping contours round an extremum. These missions were evaluated through the use of 10,000 simulations with success rates of 96:95% for low exposure waypoint navigation and 87:36% for contour mapping

Adaptive Navigation of Three-Dimensional Scalar Fields with Multiple UAVs

Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of its environment. Compared to conventional navigation methods, AN techniques can potentially reduce the time and energy needed 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 for use in navigation decisions. This dissertation presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using Santa Clara University’s (SCU) unique multilayer control architecture for groups of vehicles. Using SCU’s flexible formation control system, this work builds upon prior 2D AN research and provides 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 state-based sequencing of these primitive AN functions to execute 3D mission-level capabilities such as isosurface mapping and plume following. Functionality is verified using high-fidelity simulations of multivehicle drone clusters which account for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise. This dissertation 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

The Isoline Tracking in Unknown Scalar Fields with Concentration Feedback

The isoline tracking of this work is concerned with the control design for a sensing vehicle to track a desired isoline of an unknown scalar field. To this end, we propose a simple PI-like controller for a Dubins vehicle in the GPS-denied environments. Our key idea lies in the design of a novel sliding surface based error in the standard PI controller. For the circular field, we show that the P-like controller can globally regulate the vehicle to the desired isoline with the steady-state error that can be arbitrarily reduced by increasing the P gain, and is eliminated by the PI-like controller. For any smoothing field, the P-like controller is able to achieve the local regulation. Then, it is extended to the cases of a single-integrator vehicle and a doubleintegrator vehicle, respectively. Finally, the effectiveness and advantages of our approaches are validated via simulations on the fixed-wing UAV and quadrotor simulators

Sensor-based formation control using a generalised rigidity framework and passivity techniques

The research in this thesis addresses the subject of sensor-based formation control for a network of autonomous agents. The task of formation control involves the stabilisation of the agents to a desired set of relative states, with the possible additional objective of manoeuvring the agents while maintaining this formation. Although the formation control challenge has been widely studied in the literature, many existing control strategies are based on full state information, and give little consideration to the sensor modalities available for the task. The focus of this thesis lies in the use of a generic arrangement of partial state measurements as can commonly be acquired by onboard sensors; for example, time-of-flight sensors can be used to measure the distances between vehicles, and onboard cameras can provide the bearing from one vehicle to each of the others. Particular aspects of the problem that are addressed in this thesis include (i) ways of modelling the formation control task, (ii) methods of analysing the system's behaviour, and (iii) the design of a formation control scheme based on generic arrangements of sensors that provide only partial position information. A key contribution in this thesis is a generalisation of the classical notion of rigidity, which considers the use of distance constraints between agents in R^2 or R^3 to specify a rigid body (or formation). This enables the concept of rigidity to be applied to agent networks involving a variety of (possibly non-Euclidean) state-spaces, with a generic set of state constraints that may, for example, include bearings between agents as well as distances. I demonstrate that this framework is very well-suited for modelling a wide variety of formation control problems (addressing goal (i) above), and I extend several fundamental results from classical rigidity theory in order to provide significant insight for system analysis (addressing goal (ii) above). To design a formation control scheme that uses generic partial position measurements (addressing goal (iii) above), I employ a modular passivity-based approach that is developed using the bondgraph modelling formalism. I illustrate how adaptive compensation can be incorporated into this design approach in order to account for the unknown position information that is not available from the onboard sensors. Although formation control is the subject of this thesis, it should be noted that the rigidity-based and passivity-based frameworks developed here are quite general and may be applied to a wide range of other problems