Optimal Partitioning of a Surveillance Space for Persistent Coverage Using Multiple Autonomous Unmanned Aerial Vehicles: An Integer Programming Approach

Unmanned aerial vehicles (UAVs) are an essential tool for the battle eld commander in part because they represent an attractive intelligence gathering platform that can quickly identify targets and track movements of individuals within areas of interest. In order to provide meaningful intelligence in near-real time during a mission, it makes sense to operate multiple UAVs with some measure of autonomy to survey the entire area persistently over the mission timeline. This research considers a space where intelligence has identi ed a number of locations and their surroundings that need to be monitored for a period of time. An integer program is formulated and solved to partition this surveillance space into the minimum number of subregions such that these locations fall outside of each partitioned subregion for e cient, persistent surveillance of the locations and their surroundings. Partitioning is followed by a UAV-to-partitioned subspace matching algorithm so that each subregion of the partitioned surveillance space is assigned exactly one UAV. Because the size of the partition is minimized, the number of UAVs used is also minimized

Next-Generation Public Safety Systems Based on Autonomous Vehicles and Opportunistic Communications

An emergency scenario is characterized by the unpredictability of the environment conditions and by the scarcity of the available communication infrastructures. After a natural or human disaster, the main public and private infrastructures are partially damaged or totally destroyed. These infrastructures include roads, bridges, water supplies, electrical grids, telecommunications and so on. In these conditions, the first rescue operations executed by the public safety organizations can be very difficult, due to the unpredictability of the disaster area environment and the lack in the communications systems. The aim of this work is to introduce next-generation public safety systems where the main focus is the use of unmanned vehicles that are able to exploit the self-organizing characteristics of such autonomous systems. With the proposed public safety systems, a team of autonomous vehicles will be able to overcome the hazardous environments of a post disaster scenario by introducing a temporary dynamic network infrastructure which enables the first responders to cooperate and to communicate with the victims involved. Furthermore, given the pervasive penetration of smart end-user devices, the emergence of spontaneous networks could constitute promising solutions to implement emergency communication systems. With these systems the survivors will be able to self-organize in a communication network that allows them to send alerts and information messages towards the rescue teams, even in absence of communication infrastructures

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

Robustness of Mission Plans for Unmanned Aircraft.

This thesis studies the robustness of optimal mission plans for unmanned aircraft. Mission planning typically involves tactical planning and path planning. Tactical planning refers to task scheduling and in multi aircraft scenarios also includes establishing a communication topology. Path planning refers to computing a feasible and collision-free trajectory. For a prototypical mission planning problem, the traveling salesman problem on a weighted graph, the robustness of an optimal tour is analyzed with respect to changes to the edge costs. Specifically, the stability region of an optimal tour is obtained, i.e., the set of all edge cost perturbations for which that tour is optimal. The exact stability region of solutions to variants of the traveling salesman problems is obtained from a linear programming relaxation of an auxiliary problem. Edge cost tolerances and edge criticalities are derived from the stability region. For Euclidean traveling salesman problems, robustness with respect to perturbations to vertex locations is considered and safe radii and vertex criticalities are introduced. For weighted-sum multi-objective problems, stability regions with respect to changes in the objectives, weights, and simultaneous changes are given. Most critical weight perturbations are derived. Computing exact stability regions is intractable for large instances. Therefore, tractable approximations are desirable. The stability region of solutions to relaxations of the traveling salesman problem give under approximations and sets of tours give over approximations. The application of these results to the two-neighborhood and the minimum 1-tree relaxation are discussed. Bounds on edge cost tolerances and approximate criticalities are obtainable likewise. A minimum spanning tree is an optimal communication topology for minimizing the cumulative transmission power in multi aircraft missions. The stability region of a minimum spanning tree is given and tolerances, stability balls, and criticalities are derived. This analysis is extended to Euclidean minimum spanning trees. This thesis aims at enabling increased mission performance by providing means of assessing the robustness and optimality of a mission and methods for identifying critical elements. Examples of the application to mission planning in contested environments, cargo aircraft mission planning, multi-objective mission planning, and planning optimal communication topologies for teams of unmanned aircraft are given.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120837/1/mniendo_1.pd

Reactive evolutionary path planning for autonomous surface vehicles in lake environments.

Autonomous Surface Vehicles (ASVs) have found a lot of promising applications in aquatic environments, i.e., sea, lakes, rivers, etc. They can be used for applications of paramount importance, such as environmental monitoring of water resources, and for bathymetry to study the characteristics of the basing of a lake/sea or for surveillance in patrol missions, among others. These vehicles can be built with smaller dimensions when compared to regular ships since they do not need an on-board crew for operation. However, they do require at least a telemetry control as well as certain intelligence for making decisions and responding to changing scenarios. Water resources are very important in Paraguay since they provide fresh water for its inhabitants and they are crucial for the main economic activities such as agriculture and cattle raising. Furthermore, they are natural borders with the surrounding countries, and consequently the main transportation route for importing/exporting products. In fact, Paraguay is the third country in the world with the largest fleet of barges after USA and China. Thus, maintaining and monitoring the environmental conditions of these resources is key in the development of the country. This work is focused on the maintenance and monitoring of the greatest lake of the country called Ypacarai Lake. In recent years, the quality of its water has been seriously degraded due to the pollution caused by the low control of the dumping of waste thrown into the Lake. Since it is also a national icon, the government of Paraguay has put a lot of effort in recovering water quality of the Lake. As a result, it is monitored periodically but using manual procedures. Therefore, the primary objective of this work is to develop these monitoring tasks autonomously by means of an ASV with a suitable path planning strategy. Path planning is an active research area in robotics. A particular case is the Coverage Path Planning (CPP) problem, where an algorithm should find a path that achieves the best coverage of the target region to be monitored. This work mainly studies the global CPP, which returns a suitable path considering the initial conditions of the environment where the vehicle moves. The first contribution of this thesis is the modeling of the CPP using Hamiltonian Circuits (HCs) and Eulerian Circuits (ECs). Therefore, a graph adapted to the Ypacarai Lake is created by using a network of wireless beacons located at the shore of the lake, so that they can be used as data exchange points between a control center and the ASV, and also as waypoints. Regarding the proposed modeling, HCs and ECs are paths that begin and end at the same point. Therefore, the ASV travels across a given graph that is defined by a set of wireless beacons. The main difference between HC and EC is that a HC is a tour that visits each vertex only once while EC visits each edge only once. Finding optimal HCs or ECs that minimize the total distance traveled by the ASV are very complex problems known as NP-complete. To solve such problems, a meta-heuristic algorithm can be a suitable approach since they provide quasi-optimal solutions in a reasonable time. In this work, a GA (Genetic Algorithm) approach is proposed and tested. First, an evaluation of the performance of the algorithm with different values of its hyper-parameters has been carried out. Second, the proposed approach has been compared to other approaches such as randomized and greedy algorithms. Third, a thorough comparison between the performance of HC and EC based approaches is presented. The simulation results show that EC-based approach outperforms the HC-based approach almost 2% which in terms of the Lake size is about 1.4 km2 or 140 ha (hectares). Therefore, it has been demonstrated that the modeling of the problem as an Eulerian graph provides better results. Furthermore, it has been investigated the relationship between the number of beacons to be visited and the distance traveled by the ASV in the EC-based approach. Findings indicate that there is a quasi-lineal relationship between the number of beacons and the distance traveled. The second contribution of this work is the development of an on-line learning strategy using the same model but considering dynamic contamination events in the Lake. Dynamic events mean the appearance and evolution of an algae bloom, which is a strong indicator of the degradation of the lake. The strategy is divided into two-phases, the initial exploration phase to discover the presence of the algae bloom and next the intensification phase to focus on the region where the contamination event is detected. This intensification effect is achieved by modifying the beacon-based graph, reducing the number of vertices and selecting those that are closer to the region of interest. The simulation results reveal that the proposed strategy detects two events and monitors them, keeping a high level of coverage while minimizing the distance traveled by the ASV. The proposed scheme is a reactive path planning that adapts to the environmental conditions. This scheme makes decisions in an autonomous way and it switches from the exploratory phase to the intensification phase depending on the external conditions, leading to a variable granularity in the monitoring task. Therefore, there is a balance between coverage and the energy consumed by the ASV. The main benefits obtained from the second contribution includes a better monitoring in the quality of water and control of waste dumping, and the possibility to predict the appearance of algae-bloom from the collected environmental data

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

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

A Heuristic Method for Task Selection in Persistent ISR Missions Using Autonomous Unmanned Aerial Vehicles

The Persistent Intelligence, Surveillance, and Reconnaissance (PISR) problem seeks to provide timely collection and delivery of data from prioritized ISR tasks using an autonomous Unmanned Aerial Vehicle (UAV). In the literature, PISR is classified as a type of Vehicle Routing Problem (VRP), often called by other names such as persistent monitoring, persistent surveillance, and patrolling. The objective of PISR is to minimize the weighted revisit time to each task (called weighted latency) using an optimal task selection algorithm. In this research, we utilize the average weighted latency as our performance metric and investigate a method for task selection called the Maximal Distance Discounted and Weighted Revisit Period (MD2WRP) utility function. The MD2WRP function is a heuristic method of task selection that uses n+1 parameters, where n is the number of PISR tasks. We develop a two-step optimization method for the MD2WRP parameters to deliver optimal latency performance for any given task configuration, which accommodates both single and multi-vehicle scenarios. To validate our optimization method, we compare the performance of MD2WRP to common Traveling Salesman Problem (TSP) methods for PISR using different task configurations. We find that the optimized MD2WRP function is competitive with the TSP methods, and that MD2WRP often results in steady-state task visit sequences that are equivalent to the TSP solution for a single vehicle. We also compare MD2WRP to other utility methods from the literature, finding thatMD2WRP performs on par with or better than these other methods even when optimizing only one of its n + 1 parameters. To address real-world operational factors, we test MD2WRP with Dubins constraints, no-y zones in the operational area, return-to-base requirements, and the addition and removal of vehicles and tasks mid-mission. For each operational factor, we demonstrate its effect on PISR task selections using MD2WRP and how MD2WRP needs to be modified, if at all, to compensate. Finally, we make practical suggestions about implementing MD2WRP for flight testing, outline potential areas for future study, and offer recommendations about the conduct of PISR missions in general