7 research outputs found

    Rotorcraft Flight Dynamics and Control in Wind for Autonomous Sampling of Spatiotemporal Processes

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    In recent years, there has been significant effort put into the design and use small, autonomous, multi-agent, aerial teams for a variety of military and commercial applications. In particular, small multi-rotor systems have been shown to be especially useful for carrying sensors as they have the ability to rapidly transit between locations as well as hover in place. This dissertation seeks to use multi-agent teams of autonomous rotorcraft to sample spatiotemporal fields in windy conditions. For many sampling objectives, there is the problem of how to accomplish the sampling objective in the presence of strong wind fields caused by external means or by other rotorcraft flying in close proximity. This dissertation develops several flight control strategies for both wind compensation, using nonlinear control techniques, and wind avoidance, using artificial potential-based control. To showcase the utility of teams of unmanned rotorcraft for spatiotemporal sampling, optimal algorithms are developed for two sampling objectives: (1) sampling continuous spatiotemporal fields modeled as Gaussian processes, and (2) optimal motion planning for coordinated target detection, which is an example of a discrete spatiotemporal field. All algorithms are tested in simulation and several are tested in a motion capture based experimental testbed

    Spatio-Temporal Wind Modeling for UAV Simulations

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    Wind affects the stability and maneuverability of UAVs, which can be particularly dangerous when operating near obstacles or each other. In order to test the effectiveness of formation control laws and the impact of windy environments on the vehicles, spatio-temporal wind fields must be modeled. Each vehicle within the formation experiences unique wind conditions, but these conditions are correlated to the conditions experienced by the other vehicles. This report develops a spatio-temporal model for over-land and over-water environments that produces a representative wind field capable of running on a personal computer that also includes turbulence and gusting

    Path planning, flow estimation, and dynamic control for underwater vehicles

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    Underwater vehicles such as robotic fish and long-endurance ocean-sampling platforms operate in challenging fluid environments. This dissertation incorporates models of the fluid environment in the vehicles' guidance, navigation, and control strategies while addressing uncertainties associated with estimates of the environment's state. Coherent flow structures may be on the same spatial scale as the vehicle or substantially larger than the vehicle. This dissertation argues that estimation and control tasks across widely varying spatial scales, from vehicle-scale to long-range, may be addressed using common tools of empirical observability analysis, nonlinear/non-Gaussian estimation, and output-feedback control. As an application in vehicle-scale flow estimation and control, this dissertation details the design, fabrication, and testing of a robotic fish with an artificial lateral-line inspired by the lateral-line flow-sensing organ present in fish. The robotic fish is capable of estimating the flow speed and relative angle of the oncoming flow. Using symmetric and asymmetric sensor configurations, the robot achieves the primitive fish behavior called rheotaxis, which describes a fish's tendency to orient upstream. For long-range flow estimation and control, path planning may be accomplished using observability-based path planning, which evaluates a finite set of candidate control inputs using a measure related to flow-field observability and selects an optimizer over the set. To incorporate prior information, this dissertation derives an augmented observability Gramian using an optimal estimation strategy known as Incremental 4D-Var. Examination of the minimum eigenvalue of an empirical version of this Gramian yields a novel measure for path planning, called the empirical augmented unobservability index. Numerical experiments show that this measure correctly selects the most informative paths given the prior information. As an application in long-range flow estimation and control, this dissertation considers estimation of an idealized pair of ocean eddies by an adaptive Lagrangian sensor (i.e., a platform that uses its position data as measurements of the fluid transport, after accounting for its own control action). The adaptive sampling is accomplished using the empirical augmented unobservability index, which is extended to non-Gaussian posterior densities using an approximate expected-cost calculation. Output feedback recursively improves estimates of the vehicle position and flow-field states

    OBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMS

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    The long-term goal of this research is to optimize estimation of an unknown flowfield using an autonomous multi-vehicle or multi-sensor system. The specific research objective is to provide theoretically justified, nonlinear control, estimation, and optimization techniques enabling a group of sensors to coordinate their motion to target measurements that improve observability of the surrounding environment, even when the environment is unknown. Measures of observability provide an optimization metric for multi-agent control algorithms that avoid spatial regions of the domain prone to degraded or ill-conditioned estimation performance, thereby improving closed-loop control performance when estimated quantities are used in feedback control. The control, estimation, and optimization framework is applied to three applications of multi-agent flowfield sensing including (1) environmental sampling of strong flowfields using multiple autonomous unmanned vehicles, (2) wake sensing and observability-based optimal control for two-aircraft formation flight, and (3) bio-inspired flow sensing and control of an autonomous unmanned underwater vehicle. For environmental sampling, this dissertation presents an adaptive sampling algorithm steering a multi-vehicle system to sampling formations that improve flowfield observability while simultaneously estimating the flow for use in feedback control, even in strong flows where vehicle motion is hindered. The resulting closed-loop trajectories provide more informative measurements, improving estimation performance. For formation flight, this dissertation uses lifting-line theory to represent a two-aircraft formation and derives optimal control strategies steering the follower aircraft to a desired position relative to the leader while simultaneously optimizing the observability of the leader's relative position. The control algorithms guide the follower aircraft to a desired final position along trajectories that maintain adequate observability and avoid areas prone to estimator divergence. Toward bio-inspired flow sensing, this dissertation presents an observability-based sensor placement strategy optimizing measures of flowfield observability and derives dynamic output-feedback control algorithms autonomously steering an underwater vehicle to bio-inspired behavior using a multi-modal artificial lateral line. Beyond these applications, the broader impact of this research is a general framework for using observability to assess and optimize experimental design and nonlinear control and estimation performance

    Cooperative Vehicle Tracking in Large Environments

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    Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

    Cooperative Vehicle Tracking in Large Environments

    Get PDF
    Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation
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