Randy Beard

Distributed information filtering using consensus filters

In this paper we present a new information consensus filter for distributed dynamic-state estimation. Estimation is handled by the traditional information filter, while communication of measurements is handled by a consensus filter. First and second-order statistics of local estimates are discussed. It is shown that local information consensus filter estimates are unbiased, and the actual variance of the local estimation errors is comparable to a centralized estimate. However, local agents believe their local estimates are less accurate.

Obstacle Avoidance For Unmanned Air Vehicles Using Image Feature Tracking

This paper discusses a computer vision algorithm and a control law for obstacle avoidance for small unmanned air vehicles using a video camera as the primary sensor. Small UAVs are used for low altitude surveillance ∞ights where unknown obstacles can be encountered. Small UAVs can be given the capability to navigate in uncertain environments if obstacles are identifled. This paper presents an obstacle detection methodology using feature tracking in a forward looking, onboard camera. Features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the salient features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially problematic areas using agglomerative clustering. The small UAV then employs a sliding mode control law in the autopilot to avoid obstacles.

Improving the performance of stabilizing controls for nonlinear systems

There are a variety of tools for computing stabilizing feedback control laws for nonlinear systems. The difficulty is that these tools usually do not take into account the performance of the control, and therefore systematic improvement of an arbitrary stabilizing control law is extremely difficult and often impossible. The objective of this article is to present a design algorithm that addresses this problem. The algorithm that we present iteratively computes a sequence of control laws with increasingly improved performance. We also consider implementation issues and discuss some of the successes and difficulties that we have encountered. Finally, we present a number of illustrative examples and compare our algorithm with perturbation methods.

Multiple UAV path planning using anytime algorithms

We address the problem of generating feasible paths from a given start location to a goal configuration for multiple unmanned aerial vehicles (UAVs) operating in an obstacle rich environment that consist of static, pop-up and moving obstacles. The UAVs have limited sensor and communication ranges, when they detect a pop-up or a moving obstacle that is in the collision course with the UAV flight path, then it has to replan a new optimal path from its current location to the goal. Determining optimal paths with short time intervals is not feasible, hence we develop anytime algorithm using particle swarm optimization that yields paths whose quality increases with increase in available computation time. To track the given path by the anytime algorithm in 3D, we developed a new uav guidance law that is based on a combination of pursuit guidance law and line of sight guidance law from missile guidance literature. Simulations are carried out to show that the anytime algorithm produces good paths in a relatively short time interval and the guidance law allows the UAVs to track the generated path

Graph-Based Observability Analysis of Bearing-Only Cooperative Localization

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.

Cooperative forest fire monitoring using multiple UAVs

This paper addresses the problem of using a team of UAVs to cooperatively monitor multiple forest fires (called hotspots) in a region. We deploy two types of agents: service agents that locally monitor the state of the hotspots and detector agents that search for hotspots and assign service agents to those that are detected. We develop an assignment scheme based on auctions to perform the assignment of a hotspot to a group of service agents. While monitoring a hotspot, service agents are required to be equally spaced around the perimeter. In order to achieve equal spacing around the hotspot perimeter, we develop a splay state controller that ensures the convergence of the service agents to the equally spaced configuration. We also address the problems associated with tracking hotspots that expand in size and possibly combine together. Simulation results are presented to validate the assignment and control algorithms.

Distributed Sequential Auctions for Multiple UAV Task Allocation

Allocating tasks efficiently to multiple UAVs with limited sensor and communication ranges is a difficult problem. In this paper, we present a distributed sequential auction scheme that takes the UAV limitations into account and provides a systematic procedure for the auction process. The task allocation scheme first validates the targets using neighbors knowledge and then, depending on the decisions obtained from neighbors, an agent decides to auction or forfeit the target. The targets detected within a sensor range are validated and auctioned sequentially. A simulation study was conducted for different sensor and communication ranges to evaluate the performance of the sequential auction scheme which was compared with a greedy strategy and a simple distributed auction scheme. The results show that the sequential auction scheme performs better than the simple distributed auction and greedy strategy.

Ensuring stability of state-dependent Riccati equation controllers via satisficing

Controls based on solutions to the state-dependent Riccati equation (SDRE) have been shown to offer high performance, but they suffer from unproven stability properties. This paper combines SDRE with satisficing, a novel clf-based approach which analytically guarantees stability. Essentially, the SDRE controller is projected point-wise onto the satisficing set. It is shown that this projection onto a stabilizing set in the control space can be solved analytically, and an example demonstrates the performance of the resulting SDRE-satisficing controllers.

Relative navigation and control of a hexacopter

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.

Differential flatness based control of a rotorcraft for aggressive maneuvers

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.