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The purpose of this tutorial paper is to present an application example for the MultiUAV cooperative control simulation. MultiUAV has been used to simulate a Cooperative Moving Target Engagement (CMTE) scenario, with a team of UAVs acting as a sensor and communication network to cooperatively track and attack moving ground targets. This scenario illustrates the utility of MultiUAV for cooperative control applications requiring heterogeneous vehicles with varied sensor, communication, dynamic, and weapon capabilities. A human supervisor designates one or more moving ground targets for the vehicles to attack. The vehicle agents must then autonomously and cooperatively determine which vehicles will perform the required tasks, when the tasks will be performed, and what flight paths will be used. This requires assigning time-dependent cooperative and joint tasks, where multiple sub-elements of the primary task must be accomplished by different vehicles, for any of the tasks to have value. This tutorial focuses on the unique requirements of the CMTE scenario and how they are addressed in the MultiUAV simulation.
An authoritative reference on cooperative decision and control of unmanned aerial vehicles.
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
We investigate optimal estimation for both the position and the velocity of the ground moving target (GMT) by employing sensors composed of unmanned aerial vehicles (UAVs). The problem is the cooperative sensing by the UAVs, in terms of their location geometries to achieve optimal estimation of the GMT. Based on the Cramer-Rao bound, we are able to derive the minimum achievable error variance in estimation of the position and the velocity of the GMT, and obtain the optimal geometries of the UAV sensors via minimization of the minimum achievable error variance for unbiased estimation commanded by the Cramer-Rao bound. Our solution is complete that encompasses various situations for the GMT, and the number of UAV sensors.
The ability to track targets using Unmanned Aerial Vehicles (UAVs) has a wide range of civilian and military applications. For example, for military personnel, it is critical to track and locate a variety of objects, including the movement of enemy vehicles. For civilian applications, we can easily find UAVs performing tasks related to land survey, weather forecasting, search and rescue missions, and monitoring farm crops. This study presents a novel method for determining the locations of moving ground-based targets using UAVs and human operators. In previous research, Sharma et al. [1] developed a vision-based target tracking algorithm. They used a Kalman-filter to estimate the target's position and velocity. An information-filter was used to control the sensor. Targets were geo-localized using the pixel locations of the targets in an image. The measurements of the UAV position, altitude, and camera pose angle along with the information embedded in the image provide the required input to an estimator to geo-locate ground targets. Using the highly sophisticated skills of humans for sensing environments, we are interested in integrating the abilities of human operators as a part of sensor network. The main contribution of this thesis is a cyber-physical system developed for reducing the localization errors of targets observed by either UAVs or humans working cooperatively. In particular, in the process of developing the system, we developed (1) an Extended Kalman Filter (EKF) based algorithm to estimate the positions of multiple targets, (2) a human sensor model using neural networks, and (3) a weighted filter to fuse local target estimations from multiple UAVs. Human sensor inputs were utilized to improve the geo localization accuracy of target position estimates. This technique requires operators to be equipped with an Android device; providing operators an easy access to Google map and Global Positioning System (GPS); such that they can specify a target's position on the map. Each sensor, UAVs or human operators, exchanges data through a Wi-Fi sensor network. A central station is used to collect the information observed by independent sensors for data fusion and combines them to generate more accurate estimates that would not be available from any single UAV or a human operator. The capability of the system was demonstrated using simulation results and Android hardware.
This book highlights cooperative coverage control approaches of multi-agent systems in uncertain environments and their applications in various fields. A novel theoretical formulation of multi-agent coverage is proposed to fulfill the coverage task via divide-and-conquer scheme. By taking workload partition and sweeping operations simultaneously, a distributed sweep coverage algorithm of multi-agent systems is developed to cooperatively complete the workload on the given region, and its input-to-state stability is guaranteed in theory. Moreover, the coverage performance is evaluated by estimating the error between the actual coverage time and the optimal time. Three application scenarios are presented to demonstrate the advantages of cooperative coverage control approaches in missile interception, intelligent transportation systems and environment monitoring, respectively.
This report addresses the formulation of a general theoretical framework for issues unique to cooperative control. The approach taken is to unify the fundamental principles of control Lyapunov functions, potential field theory, and the so-called optimal return function. These three principles are woven together to achieve an analytically vigorous formulation that addresses the required functionality of cooperative control problems. The development is prepared in the context of a multiple UAV cooperative ground moving target engagement scenario.
Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery Authoritative resource offering coverage of communication, surveillance, and delivery problems for teams of unmanned aerial vehicles (UAVs) Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery studies various elements of deployment of networks of unmanned aerial vehicle (UAV) base stations for providing communication to ground users in disaster areas, covering problems like ground traffic monitoring, surveillance of environmental disaster areas (e.g. brush fires), using UAVs in rescue missions, converting UAV video surveillance, and more. The work combines practical problems, implementable and computationally efficient algorithms to solve these problems, and mathematically rigorous proofs of each algorithm’s convergence and performance. One such example provided by the authors is a novel biologically inspired motion camouflage algorithm to covert video surveillance of moving targets by an unmanned aerial vehicle (UAV). All autonomous navigation and deployment algorithms developed in the book are computationally efficient, easily implementable in engineering practice, and based only on limited information on other UAVs of each and the environment. Sample topics discussed in the work include: Deployment of UAV base stations for communication, especially with regards to maximizing coverage and minimizing interference Deployment of UAVs for surveillance of ground areas and targets, including surveillance of both flat and uneven areas Navigation of UAVs for surveillance of moving areas and targets, including disaster areas and ground traffic monitoring Autonomous UAV navigation for covert video surveillance, offering extensive coverage of optimization-based navigation Integration of UAVs and public transportation vehicles for parcel delivery, covering both one-way and round trips Professionals in navigation and deployment of unmanned aerial vehicles, along with researchers, engineers, scientists in intersecting fields, can use Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery to gain general knowledge on the subject along with practical, precise, and proven algorithms that can be deployed in a myriad of practical situations.
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.