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

Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments

Distributed Motion Planning for Multiple Quadrotors in Presence of Wind Gusts

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans