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Trevor Smith; Yuhao Chen; Nathan Hewitt; Boyi Hu; Yu Gu. Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences. International Journal of Social Robotics 2021, 1 -18.
AMA StyleTrevor Smith, Yuhao Chen, Nathan Hewitt, Boyi Hu, Yu Gu. Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences. International Journal of Social Robotics. 2021; ():1-18.
Chicago/Turabian StyleTrevor Smith; Yuhao Chen; Nathan Hewitt; Boyi Hu; Yu Gu. 2021. "Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences." International Journal of Social Robotics , no. : 1-18.
A waypoint planning algorithm for an unmanned aerial vehicle (UAV) is presented that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multisensor fusion of a fisheye camera, 3-D light detection and ranging, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3-D map of the environment (e.g., from simultaneous localization and mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.
Matteo De Petrillo; Jared Beard; Yu Gu; Jason N. Gross. Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment. IEEE Aerospace and Electronic Systems Magazine 2021, 36, 6 -16.
AMA StyleMatteo De Petrillo, Jared Beard, Yu Gu, Jason N. Gross. Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment. IEEE Aerospace and Electronic Systems Magazine. 2021; 36 (6):6-16.
Chicago/Turabian StyleMatteo De Petrillo; Jared Beard; Yu Gu; Jason N. Gross. 2021. "Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment." IEEE Aerospace and Electronic Systems Magazine 36, no. 6: 6-16.
The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ~97% over 650 m drives on rough terrain.
Cagri Kilic; Nicholas Ohi; Yu Gu; Jason Gross. Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization. IEEE Robotics and Automation Letters 2021, 6, 4782 -4789.
AMA StyleCagri Kilic, Nicholas Ohi, Yu Gu, Jason Gross. Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization. IEEE Robotics and Automation Letters. 2021; 6 (3):4782-4789.
Chicago/Turabian StyleCagri Kilic; Nicholas Ohi; Yu Gu; Jason Gross. 2021. "Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization." IEEE Robotics and Automation Letters 6, no. 3: 4782-4789.
This work aims at developing an efficient path planning algorithm for the driving objective of a Martian day (sol) that can take into account terrain information for application to the proposed Mars Sample Return (MSR) mission. To prepare the planning process for one sol (i.e., with a limited time allocated to driving), a map of expected rover velocity over a chosen area is constructed, obtained by combining traversability classes, rock abundance and slope at that location. The planning phase starts offline by computing several potential paths that can be traversed in one sol (i.e., a few hours), which will later provide suitable options to the rover if replanning is necessary due to unexpected mobility difficulties. Online, the rover gains information about its environment as it drives and updates the map locally if major discrepancies are found. If an update is made, the remaining driving time along the different options is recalculated and the most efficient path is chosen. The online process is repeated until the rover has reached its daily goal. When simulated on different areas at Gusev Crater, Mars, the algorithm correctly captured changes of terrain initially not mapped, and rerouted the rover to a more efficient path when necessary, in which case it effectively complied with the time constraint to reach the goal.
Gabrielle Hedrick; Nicholas Ohi; Yu Gu. Terrain-Aware Path Planning and Map Update for Mars Sample Return Mission. IEEE Robotics and Automation Letters 2020, 5, 5181 -5188.
AMA StyleGabrielle Hedrick, Nicholas Ohi, Yu Gu. Terrain-Aware Path Planning and Map Update for Mars Sample Return Mission. IEEE Robotics and Automation Letters. 2020; 5 (4):5181-5188.
Chicago/Turabian StyleGabrielle Hedrick; Nicholas Ohi; Yu Gu. 2020. "Terrain-Aware Path Planning and Map Update for Mars Sample Return Mission." IEEE Robotics and Automation Letters 5, no. 4: 5181-5188.
This paper presents the design, implementation, and evaluation of four filters for the estimation of angle of attack (AOA) and angle of sideslip (AOS) of small unmanned aerial vehicles (UAVs). Specifically, two novel filters (a complementary filter and an extended Kalman filter) are proposed and evaluated without using direct flow angle and GPS measurements; two existing AOA/AOS filters are also implemented and evaluated. All filters are designed with minimal inputs and states to ensure the ease of implementation, simplicity of tuning, and computation efficiency. Both simulation and UAV flight test results show the performance of the proposed filters. Especially, flight test results from two different UAVs (a T-tail UAV and a flying wing UAV) show that the root mean square errors of estimated inertial AOA and AOS are less than 1.5 degrees under nominal flight conditions and around 2 degrees under aggressive maneuvers compared with direct flow angle measurements.
Pengzhi Tian; Haiyang Chao; Harold Patrick Flanagan; Steven G. Hagerott; Yu Gu. Design and Evaluation of UAV Flow Angle Estimation Filters. IEEE Transactions on Aerospace and Electronic Systems 2018, 55, 371 -383.
AMA StylePengzhi Tian, Haiyang Chao, Harold Patrick Flanagan, Steven G. Hagerott, Yu Gu. Design and Evaluation of UAV Flow Angle Estimation Filters. IEEE Transactions on Aerospace and Electronic Systems. 2018; 55 (1):371-383.
Chicago/Turabian StylePengzhi Tian; Haiyang Chao; Harold Patrick Flanagan; Steven G. Hagerott; Yu Gu. 2018. "Design and Evaluation of UAV Flow Angle Estimation Filters." IEEE Transactions on Aerospace and Electronic Systems 55, no. 1: 371-383.
A fundamental aspect of biological intelligence, from microbes to megafauna, is the ability to forage for the provisions required to sustain life. This sometimes underrated ability to seek out, identify, and use objects of interest in an environment with limited prior knowledge is an important capability that is also needed by robots. Many robotics applications can be modeled as foraging problems, such as search and rescue, wildlife tracking, crop pollination and harvesting, mining and in-situ-resource utilization, and scientific data/sample collection.
Yu Gu; Jared Strader; Nicholas Ohi; Scott Harper; Kyle Lassak; Chizhao Yang; Lisa Kogan; Boyi Hu; Matthew Gramlich; Rahul Kavi; Jason Gross. Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment. IEEE Robotics & Automation Magazine 2018, 25, 93 -101.
AMA StyleYu Gu, Jared Strader, Nicholas Ohi, Scott Harper, Kyle Lassak, Chizhao Yang, Lisa Kogan, Boyi Hu, Matthew Gramlich, Rahul Kavi, Jason Gross. Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment. IEEE Robotics & Automation Magazine. 2018; 25 (3):93-101.
Chicago/Turabian StyleYu Gu; Jared Strader; Nicholas Ohi; Scott Harper; Kyle Lassak; Chizhao Yang; Lisa Kogan; Boyi Hu; Matthew Gramlich; Rahul Kavi; Jason Gross. 2018. "Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment." IEEE Robotics & Automation Magazine 25, no. 3: 93-101.
This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth-specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under-actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.
Yu Gu; Nicholas Ohi; Kyle Lassak; Jared Strader; Lisa Kogan; Alexander Hypes; Scott Harper; Boyi Hu; Matthew Gramlich; Rahul Kavi; Ryan Watson; Marvin Cheng; Jason Gross. Cataglyphis: An autonomous sample return rover. Journal of Field Robotics 2017, 35, 248 -274.
AMA StyleYu Gu, Nicholas Ohi, Kyle Lassak, Jared Strader, Lisa Kogan, Alexander Hypes, Scott Harper, Boyi Hu, Matthew Gramlich, Rahul Kavi, Ryan Watson, Marvin Cheng, Jason Gross. Cataglyphis: An autonomous sample return rover. Journal of Field Robotics. 2017; 35 (2):248-274.
Chicago/Turabian StyleYu Gu; Nicholas Ohi; Kyle Lassak; Jared Strader; Lisa Kogan; Alexander Hypes; Scott Harper; Boyi Hu; Matthew Gramlich; Rahul Kavi; Ryan Watson; Marvin Cheng; Jason Gross. 2017. "Cataglyphis: An autonomous sample return rover." Journal of Field Robotics 35, no. 2: 248-274.
This paper reports the results of a Pilot-Induced Oscillation (PIO) and human pilot control characterization study performed using flight data collected with a Remotely Controlled (R/C) unmanned research aircraft. The study was carried out on the longitudinal axis of the aircraft. Several existing Category 1 and Category 2 PIO criteria developed for manned aircraft are first surveyed and their effectiveness for predicting the PIO susceptibility for the R/C unmanned aircraft is evaluated using several flight experiments. It was found that the Bandwidth/Pitch rate overshoot and open loop onset point (OLOP) criteria prediction results matched flight test observations. However, other criteria failed to provide accurate prediction results. To further characterize the human pilot control behavior during these experiments, a quasi-linear pilot model is used. The parameters of the pilot model estimated using data obtained from flight tests are then used to obtain information about the stability of the Pilot Vehicle System (PVS) for Category 1 PIOs occurred during straight and level flights. The batch estimation technique used to estimate the parameters of the quasi-linear pilot model failed to completely capture the compatibility nature of the human pilot. The estimation results however provided valuable insights into the frequency characteristics of the human pilot commands. Additionally, stability analysis of the Category 2 PIOs for elevator actuator rate limiting is carried out using simulations and the results are compared with actual flight results.
Tanmay K. Mandal; Yu Gu. Analysis of Pilot-Induced-Oscillation and Pilot Vehicle System Stability Using UAS Flight Experiments. Aerospace 2016, 3, 42 .
AMA StyleTanmay K. Mandal, Yu Gu. Analysis of Pilot-Induced-Oscillation and Pilot Vehicle System Stability Using UAS Flight Experiments. Aerospace. 2016; 3 (4):42.
Chicago/Turabian StyleTanmay K. Mandal; Yu Gu. 2016. "Analysis of Pilot-Induced-Oscillation and Pilot Vehicle System Stability Using UAS Flight Experiments." Aerospace 3, no. 4: 42.
An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ~≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positioning.
Jason N. Gross; Ryan M. Watson; Stéphane D’Urso; Yu Gu. Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs. International Journal of Aerospace Engineering 2016, 2016, 1 -11.
AMA StyleJason N. Gross, Ryan M. Watson, Stéphane D’Urso, Yu Gu. Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs. International Journal of Aerospace Engineering. 2016; 2016 ():1-11.
Chicago/Turabian StyleJason N. Gross; Ryan M. Watson; Stéphane D’Urso; Yu Gu. 2016. "Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs." International Journal of Aerospace Engineering 2016, no. : 1-11.
Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors. 1. IntroductionAutonomous formation fight is an enabling technology for future manned and unmanned aircraft systems. Its potential benefits include energy savings and greenhouse gas reduction [1, 2], improved aircraft coordination within high density airspace [3, 4], and mixed operations of Unmanned Aerial Vehicles (UAVs) and manned aircraft [5]. Autonomous formation flight is also the foundation for autonomous aerial refueling [6] and UAV swarm operations [7].Close formation flight (CFF) is a natural and well documented phenomenon. Experimental biology research shows that certain birds earn 11.4% to 14.0% energy savings when flying in a “” shape formation [8, 9]. Similar benefits for fixed wing aircraft have also been investigated. In 2001, at NASA Dryden Flight Research Center, a demonstration of two F-18 research aircraft showed fuel savings of up to 14% during CFF [10]. In 2006 and 2013, a similar mission was conducted with multiple C-17 military aircraft which showed 10–14% fuel savings [11, 12]. This research and others [13] also showed that the trailing aircraft has to be precisely controlled at a specific location behind the leader’s wing tip to enjoy the energy savings. Therefore, precision computer control for close formation flight is a critical issue.Autonomous formation flight control has been explored using a number of different strategies such as “Multiple-Input-Multiple-Output,’’ “Leader-Follower,’’ “Cyclic,” and “Behavioral” [13]. Techniques for stability analysis of an autonomous formation have also been developed for measuring how position errors propagate form one vehicle to another in a cascaded system [14, 15].More specifically, the Leader-Follower approach has been widely accepted for aircraft formation flight due to relative simplicity where the problem can be represented as tracking problems that can be solved using standard control techniques. Compensation-type controllers [16–21], optimal control [22–25], adaptive control [26, 27], robust control [28], feedback linearization [27, 29–31], and behavioral [32] approaches have all been developed for formation flight applications for fixed wing aircraft and quadrotors.For this research, the nonlinear dynamic inversion (NLDI) control laws were inspired by the feedback linearization models of the early nineties [33, 34]. Feedback linearization is a generic description for the process of cancelling nonlinearity from all or a part of system’s differential equations to allow the use of linear approaches for controller design purposes. Input-output linearization describes the decomposition of those dynamics equations, a Multi-Input-Multi-Output (MIMO) system of equations, into linearized decoupled control laws [33]. Once simplified to linear functions, the equations can be inverted. This linearization and inversion process is known as nonlinear dynamic inversion. The main limitation of the approach is given by the necessary multiple assumptions made about the aircraft dynamics; therefore, the controller only performs as desired in a limited flight envelope. However, as shown in the technical literature, the flight envelope has been expanded greatly using adaptive control [14, 35–37], fuzzy logic [38], and neural network (NN) [34, 39] approaches.Experimental demonstrations of autonomous formation flight with fixed wing aircraft are very limited due to the complexity associated with multiple aircraft operations. Flight experimentation has been done by NASA [10, 40], DARPA [11], and academia [39, 41, 42].The research presented in this paper describes the latest results of a long-term research effort by researchers at West Virginia University (WVU) in demonstrating and analyzing autonomous close formation flight performance using small unmanned fixed wing and quadrotor aircraft. The control laws are designed using a similar method done with the YF-22 [41] and previous Phastball [43] aircraft flight test studies. This paper expands the analysis of the Phastball flight test analysis. It also adds the design of control laws, flight simulation, and performance analysis for a quadrotor platform.The main goal of this paper is to evaluate the performance of the designed formation controller from CFF flight test data. Another objective is to show the versatility of the control design by demonstrating close formation flight with two dynamically different platforms. In this effort, formation control performance is assessed and quantified by measuring how precisely the prespecified formation geometry can be maintained in level flight conditions.The paper is organized as follows. Section 2 provides a description of the formation flight controller designs. Section 3 explains the test bed designs. Section 4 discusses the simulation validation. Section 5 describes the Phastball flight testing and Section 6 discusses the experimental results. Section 7 concludes the paper with a discussion on future research directions.2. Formation Flight Controller Design2.1. Fixed Wing Controller DesignTwo WVU “Phastball” unmanned research aircraft fly in a tandem formation. The leader aircraft is flown by an operator on the ground. The follower aircraft is piloted by its onboard computer. Predetermined formation geometry is maintained by the flight control laws. The geometry is defined by vertical, , lateral, , and forward, , clearance from the leader’s GPS location. The orientation of the geometry is determined by the leader’s azimuth angle, , as illustrated in Figure 1.Figure 1: Formation flight geometry [43].The lateral, , forward, , and vertical, , distance errors are measured from the trailing aircraft’s desired position to its actual position:where , , and are the aircraft positions in a Local Tangent Plane (LTP) as measured by the GPS receivers. Leader parameters are indicated with the subscript “.” These errors are the performance criteria for analysis. An aircraft’s azimuth angle is calculated withwhere and are the aircraft velocity along -axis and -axis of LTP.The formation flight controller contains inner and outer feedback loop structure. The outer-loop controller minimizes the distance errors. It provides the desired pitch attitude, throttle position, and roll angle references to the inner-loop controller given its relative position with respect to the formation geometry. The inner-loop control laws then track these reference inputs by commanding the control actions, the aileron, rudder, elevator surfaces, and the motor speed.The flight path roughly lies on a horizontal 2D plane. This simplifies the flight control design into two decoupled sets of equations, one vertical and one horizontal.The outer-loop controller is designed using the NLDI approach. Two assumptions were made during the controller design process. First, the derivative of the flight path angle is assumed to be zero. Second, steady wings level or coordinated turn conditions are assumed for both the leader and follower aircraft. Detailed design for the outer-loop controller was presented in [41] and the developed nonlinear control laws for the horizontal tracking problem arewhere and are the desired roll angle and thrust commands, respectively. is mass (in kg). and are the angle of attack and side slip angle, respectively; is gravity; is the flight path angle; and is the aircraft angular turn rate. is the aircraft azimuth angle. and are constants to be provided by the engine model. and are the aerodynamic coefficients for drag. The linearized horizontal formation error dynamics, and , are equated from the following compensator-type linear control laws:Vertical geometry control is performed by a linear altitude tracker to produce the desired pitch angle:where is the desired pitch angle, is the vertical distance, and represents gains which are refined through simulation.The inner-loop control laws are designed with the goal of minimizing the cost function: and are the state variables of the aircraft and the optimized control action, respectively. The longitudinal states of include the angle of attack, ; pitch rate, ; and pitch angle, . Lateral-directional states of include side-slip angle, ; roll rate, ; roll angle, ; yaw rate, ; and yaw angle, . and are positive definite weighting matrices. The optimized control action, , enables the aircraft to track the desired outer-loop angles, pitch, , and roll, . The control action of the tracker is expressed asin the lateral direction and in the longitudinal direction, respectively. is the aileron surface deflection, is the rudder surface deflection, and is the elevator surface deflection. is the matrix of feedback gains associated with the difference between the desired outer-loop angles and the actual angles. is the matrix of feedback gains for the rest of the aircraft states. Simulation is used to affirm the inner-loop gains (8) and (9) and then, iteratively, for adjusting the outer-loop gains (5) and (6) to refine controller performance. The refined gains are shown in Table 1.Table 1: Phastball control gains.2.2. Quadrotor Controller DesignNEO quadrotors fly in a Leader-Follower configuration behind a leader as shown in Figure 2. The leader, red, can be a virtual or real object. The c
Caleb Rice; Yu Gu; Haiyang Chao; Trenton Larrabee; Srikanth Gururajan; Marcello Napolitano; Tanmay Mandal; Matthew Rhudy. Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds. International Journal of Aerospace Engineering 2016, 2016, 1 -15.
AMA StyleCaleb Rice, Yu Gu, Haiyang Chao, Trenton Larrabee, Srikanth Gururajan, Marcello Napolitano, Tanmay Mandal, Matthew Rhudy. Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds. International Journal of Aerospace Engineering. 2016; 2016 ():1-15.
Chicago/Turabian StyleCaleb Rice; Yu Gu; Haiyang Chao; Trenton Larrabee; Srikanth Gururajan; Marcello Napolitano; Tanmay Mandal; Matthew Rhudy. 2016. "Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds." International Journal of Aerospace Engineering 2016, no. : 1-15.
This article considers a novel approach to using global positioning system (GPS) signal strength readings and estimated velocity vector for estimating the attitude of a small fixed-wing unmanned aerial vehicle (UAV). This approach has the benefit being able to estimate full position, velocity and attitude states of a UAV using only the data from a single GPS receiver and antenna. Two different approaches for utilizing GPS signal strength within measurement updates for UAV attitude in a nonlinear Kalman filter are discussed and assessed using recorded UAV flight data. Comparisons of UAV pitch and roll estimates against measurements from a high-grade mechanical gyroscope are used to show that approximately 5° error with respect to both mean and standard-deviation on both axes is achievable.
Jason Gross; Yu Gu; Matthew Rhudy. Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements. Aerospace 2016, 3, 14 .
AMA StyleJason Gross, Yu Gu, Matthew Rhudy. Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements. Aerospace. 2016; 3 (2):14.
Chicago/Turabian StyleJason Gross; Yu Gu; Matthew Rhudy. 2016. "Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements." Aerospace 3, no. 2: 14.
A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.
Yu Gu; Jason Gross; Matthew B. Rhudy; Kyle Lassak. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation. International Journal of Aerospace Engineering 2016, 2016, 1 -12.
AMA StyleYu Gu, Jason Gross, Matthew B. Rhudy, Kyle Lassak. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation. International Journal of Aerospace Engineering. 2016; 2016 ():1-12.
Chicago/Turabian StyleYu Gu; Jason Gross; Matthew B. Rhudy; Kyle Lassak. 2016. "A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation." International Journal of Aerospace Engineering 2016, no. : 1-12.
This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.
Matthew B. Rhudy; Yu Gu; Haiyang Chao; Jason Gross. Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors. Journal of Robotics 2015, 2015, 1 -12.
AMA StyleMatthew B. Rhudy, Yu Gu, Haiyang Chao, Jason Gross. Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors. Journal of Robotics. 2015; 2015 ():1-12.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu; Haiyang Chao; Jason Gross. 2015. "Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors." Journal of Robotics 2015, no. : 1-12.
This paper presents modifications to the stochastic stability lemma which is then used to estimate the convergence rate and persistent error of the linear Kalman filter online without using knowledge of the true state. Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time to monitor the performance of the filter. Through simulation of an example problem, the new method was shown to be effective in determining a bound on the estimation error that closely follows the actual estimation error. Different cases of assumed process and measurement noise covariance matrices were considered in order to study their effects on the convergence and persistent error of the Kalman filter.
Matthew B. Rhudy; Yu Gu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis 2013, 2013, 1 -9.
AMA StyleMatthew B. Rhudy, Yu Gu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis. 2013; 2013 (2):1-9.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu. 2013. "Online Stochastic Convergence Analysis of the Kalman Filter." International Journal of Stochastic Analysis 2013, no. 2: 1-9.
Optical flow has been widely used by insects and birds to support navigation functions. Such information has appealing capabilities for application to ground and aerial robots, especially for navigation and collision avoidance in urban or indoor areas. The purpose of this paper is to provide a survey of existing optical flow techniques for robotics navigation applications. Detailed comparisons are made among different optical-flow-aided navigation solutions with emphasis on the sensor hardware as well as optical flow motion models. A summary of current research status and future research directions are further discussed.
Haiyang Chao; Yu Gu; Marcello Napolitano. A Survey of Optical Flow Techniques for Robotics Navigation Applications. Journal of Intelligent & Robotic Systems 2013, 73, 361 -372.
AMA StyleHaiyang Chao, Yu Gu, Marcello Napolitano. A Survey of Optical Flow Techniques for Robotics Navigation Applications. Journal of Intelligent & Robotic Systems. 2013; 73 (1-4):361-372.
Chicago/Turabian StyleHaiyang Chao; Yu Gu; Marcello Napolitano. 2013. "A Survey of Optical Flow Techniques for Robotics Navigation Applications." Journal of Intelligent & Robotic Systems 73, no. 1-4: 361-372.
Yu Gu -Unmanned Aerial Vehicles as a Versatile Research Tool
Yu Gu. Unmanned Aerial Vehicles as a Versatile Research Tool. Journal of Aeronautics & Aerospace Engineering 2012, 01, 1 .
AMA StyleYu Gu. Unmanned Aerial Vehicles as a Versatile Research Tool. Journal of Aeronautics & Aerospace Engineering. 2012; 01 (03):1.
Chicago/Turabian StyleYu Gu. 2012. "Unmanned Aerial Vehicles as a Versatile Research Tool." Journal of Aeronautics & Aerospace Engineering 01, no. 03: 1.