Research Papers

We present a testbed for multi-agent teams through the integration of small unmanned aerial systems with small autonomous ground vehicles. Using the Quanser Autonomous Research Studio, Quanser's QDrone, and QCar, we set up an environment where researchers can run behavior models to observe the interaction between aerial and ground systems. The testbed strives to be agnostic of the programming language and lower-level autonomy so that the higher-level interactions can be studied more effectively. For the purposes of this indoor testbed, we found that both internal positioning systems and external reference systems (motion capture cameras) are required for maintaining accurate pose odometry and collecting ground-truth data. Based on the observed and collected data on the QDrone and QCar, this study aims to develop the capabilities of ground/aerial autonomous system integration, while creating a basic testbed that can serve additional research on these topics.

The objective of this research is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) to close the existing research gaps between the effects of
unexpected uncertainties and current control systems. This research investigates system modeling and the effectiveness of error elimination through adaptation for change in a
plant/system’s output. The UAV is controlled via Wi-Fi using MATLAB. The effects of disturbances are reduced significantly by using the neural network-based adaptive controllers for
high-performance tracking in the presence of the disturbances. Robust control systems with adaptive mechanisms can improve navigation performance and operation for military, reconnaissance, and surveillance applications. The application of a robust controller improves the performance by observing the system’s behavior to identifying and reducing the unpredictable effects of disturbances to achieve a better behavior and response of the system. With the data sets of flight behaviors, the neural network is suitably used to learn froma set of training patterns. System identification uses a neural network to capture the behavior of system dynamics, which assists the neural network to train itself to act as a controller. Moreover, the controller's performance is validated through simulations and experiments to demonstrate the effectiveness of improving the dynamic system's speed and stability. This research presents a flexible, robust, and effective control system model that provides additional strength and reduces disturbances.

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates and efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform environment.

The development of new control strategies for quadrotor types of unmanned aerial systems is important in meeting the existing research needs to handle undesired uncertainties for more satisfactory control systems. A Neural Network-Proportional plus Velocity (NN-PV) controller is proposed for improved stability and robustness in the presence of external disturbances and model parametric uncertainty. The experimental validation is conducted using Quanser's Autonomous Vehicles Research Studio (AVRS) for multiple-vehicle testing. The developed controller is validated through indoor flight tests. The experimental approach provides understanding and the manipulation of the controller (Angle Mode) of the QDrone. The performance of the control system demonstrates improvement of the speed and stability for the formation flight of two drones. The work presented produces a flexible, robust, and effective control system model that leads to expanded stability and reduces the effect of disturbances.

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

The paper presents research on methods of a wheeled mobile robot localization using an optical motion capture system. The results of localization based on the model of forward kinematics and odometric measurements were compared. A pure pursuit controller was used to control the robot’s behaviour in the path following tasks. The paper describes a motion capture system based on infrared cameras, including the calibration method. In addition, a method for determining the accuracy of robot location using the motion capture system, based on the Hausdorff distance, was proposed. As a result of the research it was found that the Hausdorff distance is very useful in determining the accuracy of localization of wheeled robots, especially those described by differential drive kinematics.

The article presents the research on navigation algorithms of a wheeled mobile robot with the use of a vision mapping system and the analysis of energy consumption of selected navigation algorithms, such as RRT and A-star. Obstacle maps were made with the use of an RGBW camera, and binary occupation maps were also made, which were used to determine the traffic path. To recreate the routes in hardware, a programmed Pure Pursuit controller was used. The results of navigation were compared on the basis of the forward kinematics model and odometry measurements. Quantities such as current, except (x, y, phi), and linear and angular velocities were measured in real time. As a result of the conducted research, it was found that the RRT star algorithm consumes the least energy to reach the designated target in the designated environment.

This paper presents a multiple observers based anti-disturbance control (MOBADC) scheme against multiple disturbances for a quadrotor unmanned aerial vehicle (UAV). The quadrotor UAV dynamics can be represented by the Newton’s second law and Lagrange–Euler formalism. The proposed control scheme consists of disturbance observer (DO) based controller and extended state observer (ESO) based controller, which are utilized in the position loop to mainly eliminate the cable suspended payload disturbance with partially known information and mitigate the wind disturbance with bounded variation. What is more, in order to reject the model uncertainty and disturbance moment, another ESO based controller is designed for the attitude loop. Using the proposed control scheme, the anti-disturbance performance can be significantly enhanced. Experimental results in the presence of wind disturbance, payload oscillating disturbance, and hybrid disturbances illustrate the robustness and effectiveness of the proposed method compared to the classical PID method.