Visual-inertial navigation methods have been shown to be an effective, low-cost way to operate autonomously without GPS or other global measurements, however most filtering approaches to VI suffer from observability and consistency problems. To increase robustness of the state-of-the-art methods, we propose a three-fold improvement. First, we propose the addition of a linear drag term in the velocity dynamics which improves estimation accuracy. Second, we propose the use of a partial-update formulation which limits the effect of linearization errors in partially-observable states, such as sensor biases. Finally, we propose the use of a keyframe reset step to enforce observability and consistency of the normally unobservable position and heading states. While all of these concepts have been used independently in the past, our experiments demonstrate additional strength when they are used simultaneously in a visual-inertial state estimation problem. In this paper, we derive the proposed filter and use a Monte Carlo simulation experiment to analyze the response of visual-inertial Kalman filters with the above described additions. The results of this study show that the combination of all of these features significantly improves estimation accuracy and consistency.

This paper discusses the progress made on developing a multi-rotor helicopter equipped with a vision-based ability to navigate through an a priori unknown, GPS-denied environment. We highlight the backbone of our system, the relative estimation and control. We depart from the common practice of using a globally referenced map, preferring instead to keep the position and yaw states in the EKF relative to the current map node. This relative navigation approach allows simple application of sensor updates, natural characterization of the transformation between map nodes, and the potential to generate a globally consistent map when desired. The EKF fuses view matching data from a Microsoft Kinect with more frequent IMU data to provide state estimates at rates high enough to control the vehicles fast dynamics. Although an EKF is used, a nodes and edges graph represents the map. Hardware results showing the quality of the estimates and flights with estimates in the loop are provided.

In this paper, we investigate the nonlinear observability properties of bearing-only cooperative localization. We establish a link between observability and a graph that represents measurements and communication between the robots. It is shown that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location.

We propose a new method to control a multi-rotor aerial vehicle. We show that the system dynamics are differentially flat. We utilize the differential flatness of the system to provide a feed forward input. The system model derived allows for arbitrary changes in yaw and is not limited to small roll and pitch angles. We demonstrate in hardware the ability to follow a highly maneuverable path while tracking a time-varying heading command.

Multirotor aircraft have become a popular platform for indoor flight. To navigate these vehicles indoors through an unknown environment requires the use of a SLAM algorithm, which can be processing intensive. However, their size, weight, and power capacity limit the processing capabilities available onboard. In this paper, we describe an approach to state estimation that helps to alleviate this problem. By using an improved dynamic model we show how to more accurately estimate the aircraft states than can be done with the traditional approach of integrating IMU measurements. The estimation is done with relatively infrequent corrections from accelerometers (40Hz) and even less frequent updates from a vision-based SLAM algorithm (2–5 Hz). This benefit of requiring less frequent updates from processing intensive sources comes without significant increase in the estimator's complexity.

This paper poses the cooperative perimeter-surveillance problem and offers a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members. By identifying and sharing the critical coordination information and by exploiting the known communication topology, only a small communication range is required for accurate performance. Simulation and hardware results are presented that demonstrate the applicability of the solution.

In this paper, a method for accurate path following for miniature air vehicles is developed. The method is based on the notion of vector fields, which are used to generate desired course inputs to inner-loop attitude control laws. Vector-field path-following control laws are developed for straight-line paths and circular arcs and orbits. Lyapunov stability arguments are used to demonstrate asymptotic decay of path-following errors in the presence of constant wind disturbances. Experimental flight tests have demonstrated mean path-following errors on less than one wingspan for straight-line and orbit paths and less than three wingspans for paths with frequent changes in direction.

muUAVs have found increasing utility as a platform for visual data collection about an area of interest. One user-friendly way of presenting visual data about the area of interest is to create a mosaick of all data observed by the muUAV, absolving the user from having to watch the entire flight video. In addition, because multiple frames in the video sequence will in general observe an area of interest, super-resolution techniques can be applied to yield a mosaick with higher resolution than the camera on-board the muUAV. While super-resolution and mosaicking are both well studied problems, muUAVs present some interesting challenges in combining the two capabilities. Most super-resolution algorithms work within a constrained environment where the size of the superresolved image is fixed and known a priori. Mosaicking, however, is the process of creating a dynamically-sized image from multiple fixed-sized images, where each input image could contain visual data on previously unobserved areas. In this paper, we present a system that can do both: construct a mosaick, incorporating data on previously unobserved areas, while super-resolving the mosaick for areas that are observed by multiple input images

Numerous applications require aerial surveillance. Civilian applications include monitoring forest fires, oil fields, and pipelines and tracking wildlife. Applications to homeland security include border patrol and monitoring the perimeter of nuclear power plants. Military applications are numerous. The current approach to these applications is to use a single manned vehicle for surveillance. However, manned vehicles are typically large and expensive. In addition, hazardous environments and operator fatigue can potentially threaten the life of the pilot. Therefore, there is a critical need for automating aerial surveillance using unmanned air vehicles (UAVs). This paper gives an overview of a cooperative control strategy for aerial surveillance that has been successfully flight tested on small (48-in wingspan) UAVs. Our approach to cooperative control problems can be summarized in four steps: 1) the definition of a cooperation constraint and cooperation objective; 2) the definition of a coordination variable as the minimal amount of information needed to effect cooperation; 3) the design of a centralized cooperation strategy; and 4) the use of consensus schemes to transform the centralized strategy into a decentralized algorithm. The effectiveness of the solution will be shown using both high-fidelity simulation and actual flight tests

Target acquisition and continuous surveillance using fixed-wing UAVs is a difficult task due to the many degrees of freedom inherent in aircraft and gimbaled cameras. Mini-UAVs further complicate the problem by introducing severe restrictions on the size and weight of electro-optical sensor assemblies. We present a field-tested mini-UAV gimbal mechanism and flightpath generation algorithm as well as a human-UAV interaction scheme in which the operator manually flies the UAV to produce an estimate of the target position, then allows the aircraft to fly itself and control the gimbal while the operator re fines or moves the target position as required.

This paper describes a new multi-agent test-bed developed to support research in multi-agent systems that incorporate both autonomous operation and human input. Teams compete in a modified version of the game of capture-the-flag in a maze-like environment of arbitrary complexity. Each team consists of a number of mobile robots, a human commander, and a UAV that provides essential position information. The makeup of each team, the motion constraints, and the nature of the game present a significant challenge to any multi-agent coordination scheme. This paper describes the rules of the game and the hardware and software infrastructure we have developed to play it. The paper further describes some of our work in devising an effective technique for planning paths through the maze for the ground-based robots.

We present several human-UAV interfaces that support real-time control of a small semi-autonomous UAV. These interfaces are designed for searching tasks and other missions that typically do not have a precise predetermined flight plan. We present a detailed analysis of a PDA-based interface and describe how our other interfaces relate to this analysis. We then offer quantative and qualitative performance comparisons of the interfaces, as well as an analysis of their possible real-world applications.

This paper presents a behavior-based approach to formation maneuvers for groups of mobile robots. Complex formation maneuvers are decomposed into a sequence of maneuvers between formation patterns. The paper presents three formation control strategies. The first strategy uses relative position information configured in a bidirectional ring topology to maintain the formation. The second strategy injects interrobot damping via passivity techniques. The third strategy accounts for actuator saturation. Hardware results demonstrate the effectiveness of the proposed control strategies.

Presents an end-to-end solution to the battlefield scenario where M unmanned air vehicles are assigned to strike N known targets, in the presence of dynamic threats. The problem is decomposed into the subproblems of (1) cooperative target assignment, (2) coordinated UAV intercept, (3) path planning, and (4) feasible trajectory generation. The design technique is based on a hierarchical approach to coordinated control. Detailed simulation results are presented.

Presents an end-to-end solution to the cooperative control problem represented by the scenario where M unmanned air vehicles (UAVs) are assigned to transition through N known target locations in the presence of dynamic threats. The problem is decomposed into the subproblems of: 1) cooperative target assignment; 2) coordinated UAV intercept; 3) path planning; 4) feasible trajectory generation; and 5) asymptotic trajectory following. The design technique is based on a hierarchical approach to coordinated control. Simulation results are presented to demonstrate the effectiveness of the approach.

This paper presents a decentralized formation control (the coupled dynamics approach) for executing elementary formation manoeuvres (EFM) for Hilare-type mobile robots. The concept of an EFM is presented. It is then shown that each of these EFMs possesses a common mathematical structure and thus may be executed by the same type of robot control. We present two EFM controls: the first control puts feedback on the relative motion and the global motion of each robot; and the second one adds inter-robot damping. The key advantage of our approach is the reduced communication among robots and the formation obtains certain robustness properties not available to other formation controls. We present the simulation and hardware results for each of these controls.

This paper describes two hybrid control laws for Hilare-type mobile robots, together with experimental verification of their performance. The first control is mathematically motivated. The second control is motivated by the geometry of the problem. While both controls are shown to regulate the robot to a specified position and orientation, their performance is different. To evaluate the performance of the controls, we define seven criteria that we would like the closed-loop system to exhibit. The experimental performance of the two hybrid control laws are evaluated against these criteria.

The application of a new nonlinear optimal control strategy to the station-keeping control of an underwater robotic vehicle is considered. The control approach described and demonstrated here is based upon the numerical approximation of solutions to the Hamilton-Jacobi-Bellman equation. These solutions are computed by an iterative application of Galerkin's method. Preliminary simulation results demonstrating the application of this approach to the control of an underwater vehicle in the horizontal plane are presented.