Moving Ground Vehicle Research Articles

A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-Rotor Uav

Abstract Using the context of trajectory estimation and tracking for multi-rotor UAVs, we explore the challenges in applying high-gain observers to highly dynamic systems. The multi- rotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference sys- tem. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multi-rotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback con- troller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

Vision-Based Marker-Less Landing of an Unmanned Aerial System on Moving Ground Vehicle

Current autonomous unmanned aerial systems (UASs) commonly use vision-based landing solutions that depend upon fiducial markers to localize a static or mobile landing target relative to the UAS. This paper develops and demonstrates an alternative method to fiducial markers with a combination of neural-network-based object detection and camera intrinsic properties to localize an unmanned ground vehicle (UGV) and enable autonomous landing. Implementing this visual approach is challenging given the limited compute power on board the UAS, but it is relevant for autonomous landings on targets for which affixing a fiducial marker a priori is not possible or not practical. The position estimate of the UGV is used to formulate a landing trajectory that is then input to the flight controller. Algorithms are tailored toward low size, weight, and power constraints, as all compute and sensing components weigh less than 100 g. Landings were successfully demonstrated in both simulation and experimentally on a UGV traveling in both a straight line and while turning. Simulation landings were successful at UGV speeds of up to 3.0 m/s, and experimental landings at speeds up to 1.0 m/s.

Active manipulation of a tethered drone using explainable AI

Tethered drones are currently finding a wide range of applications such as for aerial surveillance, traffic monitoring, and setting up ad-hoc communication networks. However, many technological gaps are required to be addressed for such systems. Most commercially available tethered drones hover at a certain position; however, the control task becomes challenging when the ground robot or station needs to move. In such a scenario, the drone is required to coordinate its motion with the moving ground vehicle without which the tether can destabilize the drone. Another challenging aspect is when the system is required to operate in GPS denied environments, such as in planetary exploration. In this paper, to address these issues, we take advantage of passive or force-based control in which the tension in the tether is sensed and used to drive the drone. Fuzzy logic is used to implement the force-based controller as a tool for explainable Artificial Intelligence. The proposed fuzzy logic controller takes tether force and its rate of change as the inputs and provides desired attitudes as the outputs. Via simulations and experiments, we show that the proposed controller allows effective coordination between the drone and the moving ground rover. The rule-based feature of fuzzy logic provides linguistic explainability for its decisions. Simulation and experimental results are provided to validate the novel controller. This paper additionally develops an adaptive controller for estimating unknown constant winds on these tethered drone systems using a proportional controller. The simulation results demonstrate the effectiveness of the proposed adaptive control scheme in addressing the effect of wind on a tethered drone.

Aerodynamics of Landing Maneuvering of an Unmanned Aerial Vehicle in Close Proximity to a Ground Vehicle

<div class="section abstract"><div class="htmlview paragraph">Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions. While a large body of work in this area can be found on the control aspect of the UAV landing and takeoff maneuvers, research on the aerodynamic aspects of such maneuvers is non-existent. This paper presents an in-depth computational fluid dynamics (CFD) based aerodynamic characterization of the transient flow fields associated with the landing of a hobby-model quadcopter (the UAV) on an idealized road vehicle (the GV), the 35-degree slant angle Ahmed body. Transient improved delayed detached eddy simulations (IDDES) are carried out using the commercial CFD code STAR-CCM+. Our study indicates that the pressure field is the first flow property that gets impacted by the proximity of the UAV to the GV.</div></div>

Hopf Bifurcation for UAV Path Planning in Autonomous Surveillance and Landing on an UGV

A novel path planning approach based on the bifurcation theory is proposed for a quadrotor UAV to track a ground vehicle in motion. For this purpose, a brief description of the mathematical model of the quadrotor UAV and the ground vehicle is presented, and the Hopf bifurcation method around a mobile balance point is used to design the path planning for a quadrotor UAV, and autonomously it generates a circular surveillance relative to a moving ground vehicle. The stability analysis based on Lyapunov theory is presented in order to demonstrate that the trajectory satisfies the bifurcation properties. Simulation and experimental tests are carried out to validate the proposed approach.

Hemispherical InfraRed (IR) Marker for Reliable Detection for Autonomous Landing on a Moving Ground Vehicle From Various Altitude Angles

Autonomous landing of a quadrotor aerial vehicle on a mobile ground vehicle is a key function required for heterogeneous cooperation between air and ground vehicles to extend mission range. Wide angle detection and localization of mobile landing spot can improve reliability and convenience in autonomous landing processes. In this study, we present a hemispherical InfraRed (IR) marker designed and built for reliable detection and autonomous landing from various altitude angles. IR light has higher transmittance and the hemispherical design provides wide angle radiation. We present the fabrication process of the hemispherical marker from a planar flexible film, followed by full system integration on the ground vehicle. The relatively compact (radius: 2.25 cm, height: 2.3 cm) marker is detectable from wide altitude angles (0–180°) and detectable even when covered by thin foreign objects such as fallen leaves or plastic bags. Outdoor experiments with full system integrating autonomous air and ground vehicles demonstrated the feasibility of this approach to autonomous landing for heterogeneous cooperation.

UAV Mobility model for dynamic UAV-to-car communications in 3D environments

In scenarios where there is a lack of reliable infrastructures to support car-to-car communications, Unmanned Aerial Vehicles (UAVs) can be deployed as mobile infrastructures. However, the UAVs should be deployed at adequate location and heights to maintain the coverage throughout time as the irregularities of the terrain may have a significant impact on the radio signals sent to distribute information. So, flight altitude and location should be constantly adjusted in order to avoid hilly or mountainous terrains that might hinder the Line-of-Sight (LOS). In this paper, we propose a three-dimensional mobility model to define the movement of the UAV so as to maintain good coverage levels in terms of communications with moving ground vehicles by taking into account the elevation information of the Earth’s surface and the signal power towards the different vehicles. The results showed that our proposed model is able to extend the times with connectivity between the UAV and the cars compared to a simpler two-dimensional model, which never considers the altitude, and a static model, which maintains the same UAV position from the beginning to the end of the experiment.

Sliding Mode-Based Guidance for UAV Landing on a Stationary or Moving Ground Vehicle

In this paper, the problem of guidance formulation for autonomous landing of unmanned aerial vehicles on stationary or moving or maneuvering ground vehicles at desired approach angles is considered. Nonlinear engagement kinematics have been used. While integrated nonlinear controllers have been developed in the literature for this purpose, the controller inputs often need modification of the existing autopilot structure, which is challenging. In order to avoid that, a higher-level guidance algorithm is designed in this paper leveraging a sliding mode control-based approach. In the presented guidance formulation, target-state-dependent singularity can be avoided in the guidance command. The effectiveness of the presented guidance law is verified with numerical simulation studies.

Moving Ground Target Detection With Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution

The main challenging task in moving ground target detection with seismic signals is the extraction of robust feature vectors for accurate localization of a seismic event. Toward this goal, a new feature extraction technique is proposed for seismic event detection based on time-frequency analysis implemented with the Smooth pseudo Wigner–Ville distribution (SPWVD). The time-frequency coefficients are utilized to constitute a Renyi entropy vector exploiting a probability distribution function. This entropy vector gives information on the localized measurement of a possible seismic event resulting from moving ground vehicles. The likelihood of an actual seismic event is then ensured by using a constant false alarm rate (CFAR) detector which dynamically adjusts the threshold to minimize false alarms. The proposed algorithm is tested on the benchmarked data set SITEX02 consisting of seismic signals generated by moving tracked and wheeled vehicles. Additionally, the algorithm is also applied on a data set generated by collecting the seismic signature of a moving wheeled vehicle, i.e., bus within the campus. The results are also compared with pseudo Wigner–Ville distribution (PWVD) and short-time Fourier transform (STFT)-based seismic event detection technique. Significantly improved detection results are observed for SPWVD technique. An F-score improvement of ~24% as well as lead time enhancement of twofold is observed in comparison with the PWVD for relatively weak seismic signals generated by wheeled Dragon Wagon (DW) vehicles. Similarly, the F-score improvement is of ~13% and the lead enhancement is approximately 10 s in comparison with the STFT method.

Multimodal obstacle detection in unstructured environments with conditional random fields

AbstractReliable obstacle detection and classification in rough and unstructured terrain such as agricultural fields or orchards remains a challenging problem. These environments involve large variations in both geometry and appearance, challenging perception systems that rely on only a single sensor modality. Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly be perceived as nontraversable or might even obscure actual obstacles. Likewise, traversable grass or dirt roads and obstacles such as trees and bushes might be visually ambiguous. In this paper, we combine appearance‐ and geometry‐based detection methods by probabilistically fusing lidar and camera sensing with semantic segmentation using a conditional random field. We apply a state‐of‐the‐art multimodal fusion algorithm from the scene analysis domain and adjust it for obstacle detection in agriculture with moving ground vehicles. This involves explicitly handling sparse point cloud data and exploiting both spatial, temporal, and multimodal links between corresponding 2D and 3D regions. The proposed method was evaluated on a diverse data set, comprising a dairy paddock and different orchards gathered with a perception research robot in Australia. Results showed that for a two‐class classification problem (ground and nonground), only the camera leveraged from information provided by the other modality with an increase in the mean classification score of 0.5%. However, as more classes were introduced (ground, sky, vegetation, and object), both modalities complemented each other with improvements of 1.4% in 2D and 7.9% in 3D. Finally, introducing temporal links between successive frames resulted in improvements of 0.2% in 2D and 1.5% in 3D.

Study a bearing-only moving ground target tracking problem using single seismic sensor

The problem of tracking moving ground targets using seismic sensors is considered in this paper. Noisy seismic data induced from a moving ground vehicle is detected and collected by a single, fixed, and passive three-component seismic sensor. Two Bayesian suboptimal estimator, namely the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), and the optimal Monte Carlo based particle filter (PF) were used in estimating and tracking the true angular behavior of the target. The comparison between these estimators showed that they have almost the same accuracy in estimating the mean value of the noisy target azimuth. In terms of filter consistency, EKF and PF with a number of particles (NP = 5000) are superior to the UKF estimator.

An Evaluation of Stochastic Model-Dependent and Model-Independent Glider Flight Management

This paper develops and compares two stochastic strategies that manage glider flight in a dynamic environment, to tackle an open problem concerning the necessity, applicability, complexity, and validity of using an environment model when making flight management decisions. Strategy performance is compared on two surveillance and pursuit tasks that require optimal control: the maximization of expected range while maintaining altitude within given limits, and the maximization of expected range while following a moving ground vehicle within a prescribed distance and maintaining altitude within given limits. Both tasks involve flight management decisions of glider airspeed and the time-to-spend climbing in a randomly encountered thermal. The first strategy is stochastic drift counteraction optimal control (SDCOC), a dynamic programming method that relies on a model. Here, SDCOC uses a computationally inexpensive yet accurate environment model that reflects transition probabilities between updrafts and downdrafts as well as thermal locations and strengths based on existing glider flight data. The second strategy is selective evolutionary generation, a model-free Markov chain Monte Carlo method that is not subject to the curse of dimensionality, efficiently searches for an optimal control in an online and tunable fashion, and easily adapts to a dynamic environment. However, this strategy requires a tolerance for learning and decision-space exploration. Both strategies perform satisfactorily, and showcase an environment/decision-space exploitation-exploration tradeoff.

A simultaneous trajectory generation method for quadcopter intercepting ground mobile vehicle

This article proposes a trajectory generator for quadcopter to intercept moving ground vehicle. For this air–ground interaction problem, we formulate the trajectory generation problem as quadratic dynamic programming in a moving-horizon scheme based on the quadcopter kinematics and observation to ground vehicle. The closed-form solution of quadratic dynamic programming in each iteration enables this algorithm a real-time replanning performance. Thereafter, segmented trajectory rule, inspired from commercial flight landing regular, is implemented to guarantee smoothness in approaching and interception to moving ground target from comparably far origin. Our established algorithm is verified through both simulations and experiments.

Method for effective implementation of an acoustic based Hostile Fire Detection System on moving vehicles

Due to background noise issues, effective implementation of an acoustic array as part of a Hostile Fire Detection System on moving vehicles has been a significant challenge to date. A new method utilizing both software and hardware design parameters has been developed and successfully demonstrated to be very effective at high speeds on various vehicles. This method has been shown to be effective against both flow based noise sources and acoustic wave based noise sources in low signal to noise ratio conditions. The technology will be discussed and results from measurements utilizing conformally mounted acoustic sensors on moving ground vehicles will be shown.

GNSS-, Communication-and Map-Based Control System for Initiation of a Heterogeneous Rendezvous Maneuver

Abstract: This paper demonstrates a GNSS-(Global Navigation Satellite System), communication-and map-based control system initiating a heterogeneous rendezvous maneuver between a car-like ground vehicle and a fixed-wing aircraft. The rendezvous maneuver is intended to serve as a preparation step for an autonomous landing of the aircraft on the moving ground vehicle. The long term motivation is amongst others found in energy saving potentials seen in the use of ground-based takeoff and landing (TOL) support systems. For experimental validation, model-scale vehicles are used. While the ground vehicle itself could be seen a scaled version of a larger ground vehicle with similar 2-dimensional actuation capabilities, it is also intended to be used as a mock-up for a track-based MAGLEV system, a system often proposed in literature for ground-based TOL support systems. In order to mimic the behavior of a MAGLEV track, a Virtual Track application is implemented on the ground vehicle. Within the presented setup, the set-speed of the aircraft following predefined waypoints is fixed. At the same time, the longitudinal dynamics of the ground vehicle are used to synchronize it to the aircraft. The setup proposed in this paper incorporates the use of a digital map through which synchronization is assisted and automation capabilities are extended. Experimental results are shown to evaluate the possibilities of the proposed system.

A Kirchhoff approximation-based numerical method to compute multiple acoustic scattering of a moving source

Within the scope of a study of external noise propagation from moving ground vehicles, a numerical method is developed to compute the acoustic field emitted by a moving source in the presence of scattering objects such as roads, buildings or noise-shields. This method is developed with the purpose of being used in a vehicle design process and therefore it must have a low computational cost, which requires a certain number of approximations. The case of a fixed point source is studied first then the effect of a movement of the source is taken into account through the introduction of a retarded time. The acoustic source is assumed to be represented by one or many harmonic monopoles of possibly different frequency moving with a constant speed in a quiescent flow field. Scattering from nearby perfectly reflecting objects is computed through a Kirchhoff–Helmholtz integral equation applying the Kirchhoff approximation. A ray-surface intersection algorithm to compute shadow areas is proposed. The method is validated against analytical solutions and experimental results for a fixed source, and against a higher-order finite difference time-domain method for the multiple scattering of a moving source. Results are good and show that this method can potentially be used to predict urban noise.

Coordinated standoff tracking of moving target groups using multiple UAVs

This paper presents a methodology for coordinated standoff tracking of moving target groups using multiple unmanned aerial vehicles (UAVs). The vector field guidance approach for a single UAV is first applied to track a group of targets by defining a variable standoff orbit to be followed, which can keep all targets within the field-of-view of the UAV. A new feedforward term is included in the guidance command considering variable standoff distance, and the convergence of the vector field to the standoff orbit is analyzed and enhanced by adjusting radial velocity using two active measures associated with vector field generation. Moreover, for multiple group tracking by multiple UAVs, a two-phase approach is proposed as a suboptimal solution for a Non-deterministic Polynomial-time hard (NP-hard) problem, consisting of target clustering/assignment and cooperative standoff group tracking with online local replanning. Lastly, localization sensitivity to the group of targets is investigated for different angular separations between UAVs and sensing configurations. Numerical simulations are performed using randomly moving ground vehicles with multiple UAVs to verify the feasibility and benefit of the proposed approach.

Airborne Monitoring of Ground Traffic Using Unmanned Aerial Vehicles

This article presents an airborne surveillance methodology to monitor the ground traffic and identify suspicious or abnormal behaviours using unmanned aerial vehicles. To track moving targets using an on-board moving target indicator, the tracking filter is designed along with sensor fusion and trajectory smoothing techniques to improve the estimation accuracy. Using the estimated car states, two behaviour recognition algorithms are introduced: (i) string pattern matching to find pre-defined suspicious behaviours in the car driving mode history (expressed as a string of numbers) and (ii) a statistical learning approach with domain knowledge given by road-map information. To verify the feasibility and benefits of the proposed approaches, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.

Road-map–assisted standoff tracking of moving ground vehicle using nonlinear model predictive control

This paper presents road-map-assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role in tracking performance, this paper focuses on utilizing road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is first proposed using constant curvature segments, and then nonlinear road-constrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both an extended Kalman filter and unscented Kalman filter are implemented along with the state-vector fusion technique for cooperative unmanned aerial vehicles. Lastly, nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car trajectory data in city traffic.

Vision-based target tracking with a small UAV: Optimization-based control strategies

This paper considers the problem of a small, fixed-wing UAV equipped with a gimbaled camera autonomously tracking an unpredictable moving ground vehicle. Thus, the UAV must maintain close proximity to the ground target and simultaneously keep the target in its camera׳s visibility region. To achieve this objective robustly, two novel optimization-based control strategies are developed. The first assumes an evasive target motion while the second assumes a stochastic target motion. The resulting optimal control policies have been successfully flight tested, thereby demonstrating the efficacy of both approaches in a real-world implementation and highlighting the advantages of one approach over the other.