44 research outputs found
Outdoor operations of multiple quadrotors in windy environment
Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure.
This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication.
The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals.
This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments
Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool
This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations
Model-Based Control of Flying Robots for Robust Interaction under Wind Influence
Model-Based Control of Flying Robots for Robust Interaction under Wind Influence The main goal of this thesis is to bridge the gap between trajectory tracking and interaction control for flying robots in order to allow physical interaction under wind influence by making aerial robots aware of the disturbance, interaction, and faults acting on them. This is accomplished by reasoning about the external wrench (force and torque) acting on the robot, and discriminating (distinguishing) between wind, interactions, and collisions. This poses the following research questions. First, is discrimination between the external wrench components even possible in a continuous real-time fashion for control purposes? Second, given the individual wrench components, what are effective control schemes for interaction and trajectory tracking control under wind influence? Third, how can unexpected faults, such as collisions with the environment, be detected and handled efficiently and effectively? In the interest of the first question, a fourth can be posed: is it possible to obtain a measurement of the wind speed that is independent of the external wrench? In this thesis, model-based methods are applied in the pursuit of answers to these questions. This requires a good dynamics model of the robot, as well as accurately identified parameters. Therefore, a systematic parameter identification procedure for aerial robots is developed and applied. Furthermore, external wrench estimation techniques from the field of robot manipulators are extended to be suitable for aerial robots without the need of velocity measurements, which are difficult to obtain in this context. Based on the external wrench estimate, interaction control techniques (impedance and admittance control) are extended and applied to flying robots, and a thorough stability proof is provided. Similarly, the wrench estimate is applied in a geometric trajectory tracking controller to compensate
external disturbances, to provide zero steady-state error under wind influence without the need of integral control action. The controllers are finally combined into a novel compensated impedance controller, to facilitate the main goal of the thesis. Collision detection is applied to flying robots, providing a low level reflex reaction that increases safety of these autonomous robots. In order to identify aerodynamic models for wind speed estimation, flight experiments in a three-dimensional wind tunnel were performed using a custom-built hexacopter. This data is used to investigate wind speed estimation using different data-driven aerodynamic models. It is shown that good performance can be obtained using relatively simple linear regression models. In this context, the propeller aerodynamic power model is used to obtain information about wind speed from available motor power measurements. Leveraging the wind tunnel data, it is shown that power can be used to obtain the wind speed. Furthermore, a novel optimization-based method that leverages the propeller aerodynamics model is developed to estimate the wind speed. Essentially, these two methods use the propellers as wind speed sensors, thereby providing an additional measurement independent of the external force. Finally, the novel topic of simultaneously discriminating between aerodynamic, interaction, and fault wrenches is opened up. This enables the implementation of novel types of controllers that are e.g. compliant to physical interaction, while compensating wind disturbances at the same time. The previously unexplored force discrimination topic has the potential to even open a new research avenue for flying robots
Location prediction and trajectory optimization in multi-UAV application missions
Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.Includes bibliographical references
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space