An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments

This thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework to synchronously perform shared tasks. These include aerial beamforming and coordinated mapping. It also focuses on use of multiple UAVs with the overall goal of enhancing the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. In the first part of the thesis, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size, Weight, and Power(SWaP) constrained smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone, solar rechargeable quadcopter. Real-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant, multiple UAVs to have time, phase, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs. We have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). In the second part of the thesis, we focus on a multi UAV autonomy framework for real-world applications of UAVs in perception, wireless communications, and reliable LTA missions. We present 'Simplenav,' a navigation stack for heterogeneous, multiple UAVs. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs.--Author's abstract

Multiple vehicles cooperative control is an important field of research for unmanned aerial vehicles (UAVs). In this paper, the difficulties and challenges for cooperation of multiple networked UAVs were analyzed. Based on OODA model, the cooperation process of multiple networked UAVs was presented. Furthermore, three key technologies for multiple UAVs cooperative control were proposed, which are multiple vehicles cooperative information sensing under dynamic environment, multiple vehicles distributed mission decision under dynamic network and multiple vehicle autonomous online path planning and path coordination. Finally, the main research content and trend of these key technologies were discussed.

Unmanned aerial vehicles (UAVs) are rapidly developing in major fields but also exposed it to security vulnerabilities. UAVs operate in high airborne network where it encounters many obstacles and uncertainty. And the existing path planning algorithms don’t consider path deviation attacks where the data communication is not secured. But, in multiple unmanned aerial vehicles, path planning is a distinctive optimization problem. And for swarm of unmanned aerial vehicles (UAVs) deployed in an uncertain dynamic environment, optimizing the UAV coverage area is a major research area that it needs to be addressed to support effective and secure data transmission. For optimized path planning in UAV swarm, we proposed a modified particle swarm optimization (MPSO) algorithm which encodes the multidimensional points for instant optimal path. The factors that are considered for the encoding helps to find the global optimum. And the blockchain distributed ledger in UAV nodes will protect the UAV network from security attacks such as denial-of-service and man-in-the-middle attacks. It also supports unrestricted coverage area, where the storage is made active only on demand which is dynamically cost-effective in UAV network. Simulation results shows that the proposed algorithm provides instant optimal path with unrestricted coverage aided with data security.KeywordsSwarm optimizationDenial-of-service attackMan-in-the-middle attackBlockchainData securityUnmanned aerial vehicles

Investigating the path planning of multiple unmanned aerial vehicles (UAVs) covering multiple regions, this work proposes an effective heuristic method of region coverage path planning to reduce the complexity of the problem. The proposed method decomposes the solution process into two stages. First of all, the two most important parameters affecting the performance of UAV missions were considered, namely, the flying speed and the scan width. According to these two parameters of UAVs, a new multi-regional allocation scheme based on the minimum consumption ratio was proposed. With this allocation scheme, the coverage task allocation and path pre-planning of UAVs were obtained. Then, the UAVs’ trajectory routes were optimized based on the dynamic planning algorithm to reduce the time consumption of UAVs on the transfer path between regions. The method was evaluated with numerical experiments. The results showed that the proposed method can effectively solve the path planning problem of multiple UAVs covering multiple regions. Compared with an advanced algorithm, the time consumption for homogeneous and heterogenous UAV performance was reduced by 5.1% and 3%, respectively.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

In this paper, we present a distributed control architecture for multiple Unmanned Aerial Vehicles (UAVs) to search, detect, and monitor emergency sites using camera sensors. The motivation for our current work is to meet the Air Force’s needs to quickly search and detect emergency sites, providing necessary aerial support to ground forces. The control architecture is designed for multiple independent UAVs with limited communication range. Each UAV makes decisions independently based on its sensor information and information it receives from other UAVs in its vicinity. We demonstrate the validity of our control architecture using results obtained from a graphical simulator developed in-house. In addition, we show Piccolo c flight control system simulation results, the first step toward implementing the control architecture onto a hardware unmanned aircraft system. The simulation on the graphical simulator is performed with multiple UAVs and multiple emergency sites, while the hardware simulation is done using two autonomously controlled aircraft and one emergency site.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

To archive diverse kinds of goal, multiple unmanned aerial vehicles (UAVs) need to cooperate each other. Therefore, the importance of a ground station becomes more important. This paper proposes a framework of the ground station that controls multiple and hetero UAVs. The framework contains multiple UAV control modules and UI interfaces and has the flexibility for adding diverse kinds of UAVs and embedded devices such as commercial UAVs, Arduino-based UAVs, cameras and storage devices.

Due to its high manoeuvrability, portable size, high payload capability and good expandability, Unmanned Aerial Vehicle (UAV) is capable of accomplishing varieties of tough missions. In particular, cooperative multiple UAVs can significantly increase the mission efficiency and effectiveness. In some applications with complex environments such as narrow canyon, distributed formation with adjustable shape is required. Moreover, a reliable autopilot safety system is mandatory so that the flight does not pose threats to the surrounding environment. In this research, distributed formation control of multiple UAVs with built-in comprehensive safety features for operation in complex environment is studied. Distributed formation control has been studied in an existing work by integrating proportional control into its path planner. There exists, however, steady state errors when an external reference velocity is applied to a certain UAV. In order to improve the formation control and eliminate the steady-state error, a proportional-integral (PI) controller is proposed. It is shown that the proposed distributed PI controller can stabilize the system and achieve the zero steady-state error. Numerical and hardware in the loop (HIL) simulations and flight tests are conducted to verify the effectiveness of the proposed approach. Since distributed formation is controlled by an on-board autopilot, failures may happen due to system or hardware faults. Hence, safety and robustness of the on-board autopilot become very important. Therefore a comprehensive approach on the implementation of safety operation for multiple UAVs formation in outdoor environments is proposed. A pre-flight UAV status inspection schedule and a hierarchical three layers in-flight safety architecture are developed based on a customized risk assessment matrix.

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

Unmanned aerial vehicles (UAVs), commonly known as drones, have grown in popularity and utility in a variety of applications, including surveillance, agriculture, disaster management, and communication. Path planning for unmanned aerial vehicles (UAVs) in a networked environment is a significant factor that influences the efficiency and efficacy of UAV operations. This chapter presents an overview of UAV network path planning, concentrating on essential principles, problems, and cutting-edge technologies. Because of technological breakthroughs and a wide range of applications, the use of unmanned aerial vehicles (UAVs) has grown quickly. Multiple UAVs collaborate to achieve specified objectives in UAV networks, demanding effective path-planning algorithms to enhance their performance. Path planning entails deciding on a series of actions that will allow a UAV to reach its destination while avoiding obstacles and sticking to stated limits. A UAV network is made up of many UAVs that communicate and work together to complete tasks.

As an important part of cooperative control for multiple unmanned aerial vehicles (UAVs), cooperative path planning can get optimal flight path which can satisfy different constraints. Research on cooperative path planning for multiple UAVs is summarized in this paper. Firstly, problem description and constraints are given. Then, solution frameworks and path coordination approaches are summarized. After that, several control methods commonly used in formation of multiple UAVs are introduced respectively. Lastly, possible research directions in the future time are put forward.

Path planning is considered for the aerial refueling of an unmanned aerial vehicle (UAV). The objective of the path planning considered in this study is to make a tanker and a UAV rendezvous at a preplanned point. To avoid abrupt maneuver of the UAV while minimizing energy consumption, rendezvous path planning as well as a guidance law is proposed. The proposed rendezvous path planning is constructed in two phases: approach phase which is the phase from the initial position to the transition, and rendezvous phase which is the phase from the transition to the preplanned point for rendezvous. The UAV performs speed control using the shape of the path and acceleration commands, and finally the UAV maintains the same velocity as the tanker to accomplish a refueling mission. Aerial refueling mission is generally performed between one tanker and multiple aircraft, and, therefore, a preplanned point is assigned to each UAV for the rendezvous. Numerical simulation is performed to demonstrate the performance of the proposed method.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

Fuzzy multiobjective cooperative surveillance of multiple UAVs based on distributed predictive control for unknown ground moving target in urban environment