Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints

Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distributed approach to coordinate a team of heterogeneous UAVs cooperating efficiently in patrolling missions around irregular areas, with low communication ranges and memory storage requirements. Hence it can be used with small‐scale UAVs with limited and different capabilities. The presented system uses a modular architecture and solves the problem by dividing the area between all the robots according to their capabilities. Each aerial robot performs a decomposition based algorithm to create covering paths and a ’one‐to‐one’ coordination strategy to decide the path segment to patrol. The system is decentralized and fault‐tolerant. It ensures a finite time to share information between all the robots and guarantees convergence to the desired steady state, based on the maximal minimum frequency criteria. A set of simulations with a team of quad‐rotors is used to validate the approach

Performance Guarantee of a Sub-Optimal Policy for a Discrete Markov Decision Process and Its Application to a Robotic Surveillance Problem

This dissertation deals with the development and analysis of sub-optimal decision algorithms for a collection of robots that assist a remotely located operator in perimeter surveillance. The operator is tasked with the classification of incursions across the perimeter. Whenever there is an incursion into the perimeter, a nearby Unattended Ground Sensor (UGS) signals an alert. A robot services the alert by visiting the alert location, collecting evidence in the form of video imagery, and transmitting it to the operator. The accuracy of operator's classification depends on the volume and freshness of information gathered and provided by the robots at locations where incursions occur. There are two competing needs for a robot: it needs to spend adequate time at an alert location to collect evidence for aiding the operator in accurate classification but it also needs to service other alerts as soon as possible, so that the evidence collected is relevant. The control problem is to determine the optimal amount of time a robot must spend servicing an alert. The incursions are stochastic and their statistics are assumed to be known. The control problem may be posed as a Markov Decision Problem (MDP). Dynamic Programming(DP) provides the optimal policy to the MDP. However, because of the "curse of dimensionality" of DP, finding the optimal policy is not practical in many applications. For a perimeter surveillance problem with two robots and five UGS locations, the number of states is of the order of billions. Approximate Dynamic Programming (ADP) via Linear Programming (LP) provides a way to approximate the value function and derive sub-optimal strategies. Using state partitioning and ADP, this dissertation provides different LP formulations for upper and lower bounds to the value function of the MDP, and shows the relationship between LPs and MDP. The novel features of this dissertation are (1) the derivation of a tractable lower bound via LP and state partitioning, (2) the construction of a sub-optimal policy whose performance exceeds the lower bound, and (3) the derivation of an upper bound using a non-linear programming formuation. The upper and lower bounds provides approximation ratio to the value function. Finally, illustrative perimeter surveillance examples corroborate the results derived in this dissertation

Mission planning for a multiple-UAV patrol system in an obstructed airport environment

This paper investigates using multiple unmanned aerial vehicles (UAVs) to carry out routine patrolling at an airport to enhance its perimeter security. It specifically focuses on mission planning of the system to facilitate efficient patrolling with consideration of local buildings and restricted airspace. The proposed methodology includes three aspects: 1) a vision-based set cover algorithm to construct the patrolling network, 2) an obstructed partitioning-based clustering algorithm for recharging station placement, and 3) a mixture integer quadratic programming (MIQP) algorithm to plan routes for UAVs minimizing the maximum idle time through out all patrolling waypoints. The main contribution of this work is that it provides a comprehensive mission planning solution for UAVs persistently patrolling in a complex environment characterized by blocked vision and restricted airspace. The proposed methodology is evaluated through intensive simulations in the context of the Cranfield Airport scenario.Innovate UK: 1002481

Implementing Influence Diagram Concepts to Optimize Border Patrol Operations using Unmanned Aerial Vehicles

The most common approach for border patrol operations is the use of human personnel and manned ground vehicles, which is expensive, at times inefficient and sometimes even hazardous to people involved. The length of the US border, mostly covering unpopulated areas, with harsh atmospheric conditions makes it more susceptible to illegal human activities. Automated border surveillance by unattended, fixed, ground sensors forming an electronic fence has proven expensive, inefficient and was prone to unacceptable rate of false alarms. A better approach would be using Unmanned Aerial Vehicles (UAVs) in combination with such ground sensors. This would help improve the overall effectiveness of the surveillance system as a UAV could first scan the alert area before sending in personnel and vehicles, if deemed necessary. In this thesis, we are proposing border surveillance using multiple Unmanned Aerial Vehicles (UAVs) in combination with alert stations consisting of Unattended Ground Sensors (UGSs) along the border line or fence. Upon detecting an event, an alert would be triggered by any UGS. We simulate this process by reading probability data for different timestamps from a text file. And, based on utility values of each stations, two UAVs decide on which alert stations to service

Formulation of control strategies for requirement definition of multi-agent surveillance systems

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.Ph.D

A Decomposition Strategy for Optimal Coverage of an Area of Interest using Heterogeneous Team of UAVs

In this thesis, we study the problem of optimal search and coverage with heterogeneous team of unmanned aerial vehicles (UAVs). The team must complete the coverage of a given region while minimizing the required time and fuel for performing the mission. Since the UAVs have different characteristics one needs to equalize the ratio of the task to the capabilities of each agent to obtain an optimal solution. A multi-objective task assignment framework is developed for finding the best group of agents that by assigning the optimal tasks would carry out the mission with minimum total cost. Once the optimal tasks for UAVs are obtained, the coverage area (map) is partitioned according to the results of the multi-objective task assignment. The strategy is to partition the coverage area into separate regions so that each agent is responsible for performing the surveillance of its particular region. The decentralized power diagram algorithm is used to partition the map according to the results of the task assignment phase. Furthermore, a framework for solving the task assignment problem and the coverage area partitioning problem in parallel is proposed. A criterion for achieving the minimum number of turns in covering a region a with single UAV is studied for choosing the proper path direction for each UAV. This criterion is extended to develop a method for partitioning the coverage area among multiple UAVs that minimizes the number of turns. We determine the "best" team for performing the coverage mission and we find the optimal workload for each agent that is involved in the mission through a multi-objective optimization procedure. The search area is then partitioned into disjoint subregions, and each agent is assigned to a region having an optimum area resulting in the minimum cost for the entire surveillance mission

Decentralized Control of an Energy Constrained Heterogeneous Swarm for Persistent Surveillance

Robot swarms are envisioned in applications such as surveillance, agriculture, search-and-rescue operations, and construction. The decentralized nature of swarm intelligence has three key advantages over traditional multi-robot control algorithms: it is scalable, it is fault tolerant, and it is not susceptible to a single point of failure. These advantages are critical to the task of persistent surveillance - where a number of target locations need to be visited as frequently as possible. Unfortunately, in the real world, the autonomous robots that can be used for persistent surveillance have a limited battery life (or fuel capacity). Thus, they need to abandon their surveillance duties to visit a battery swapping station (or refueling depot) a.k.a. €˜depots€™. This €˜down time€™ reduces the frequency of visitation. This problem can be eliminated if the depots themselves were autonomous vehicles that could meet the (surveillance) robots at some point along their path from one target to another. Thus, the robots would spend less time on the \u27charging\u27 (or refueling) task. In this thesis we present decentralized control algorithms, and their results, for three stages of the persistent surveillance problem. First, we consider the case where the robots have no energy constraints, and use a decentralized approach to allow the robots choose the €˜best€™ target that they should visit next. While the selection process is decentralized, the robots can communicate with all the other robots in the swarm, and let them know which is their chosen target. We then consider the energy constraints of the robots, and slightly modify the algorithm, so that the robots visit a depot before they run out of energy. Lastly, we consider the case where the depots themselves can move, and communicate with the robots to pick a location and time to meet, to be able to swap the empty battery of a robot, with a fresh one. The goal of persistent surveillance is to visit target locations as frequently as possible, and thus, the performance measurement parameter is chosen to be the median frequency of visitation for all target locations. We evaluate the performance of the three algorithms in an extensive set of simulated experiments

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

COUNTER-UXS ENERGY AND OPERATIONAL ANALYSIS

At present, there exists a prioritization of identifying novel and innovative approaches to managing the small Unmanned Aircraft Systems (sUAS) threat. The near-future sUAS threat to U.S. forces and infrastructure indicates that current Counter-UAS (C-UAS) capabilities and tactics, techniques, and procedures (TTPs) need to evolve to pace the threat. An alternative approach utilizes a networked squadron of unmanned aerial vehicles (UAVs) designed for sUAS threat interdiction. This approach leverages high performance and Size, Weight, and Power (SWaP) conformance to create less expensive, but more capable, C-UAS devices to augment existing capabilities. This capstone report documents efforts to develop C-UAS technologies to reduce energy consumption and collaterally disruptive signal footprint while maintaining operational effectiveness. This project utilized Model Based System Engineering (MBSE) techniques to explore and assess these technologies within a mission context. A Concept of Operations was developed to provide the C-UAS Operational Concept. Operational analysis led to development of operational scenarios to define the System of Systems (SoS) concept, operating conditions, and required system capabilities. Resource architecture was developed to define the functional behaviors and system performance characteristics for C-UAS technologies. Lastly, a modeling and simulation (M&S) tool was developed to evaluate mission scenarios for C-UAS.Outstanding ThesisCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Utilization Of A Large-Scale Wireless Sensor Network For Intrusion Detection And Border Surveillance

To control the border more effectively, countries may deploy a detection system that enables real-time surveillance of border integrity. Events such as border crossings need to be monitored in real time so that any border entries can be noted by border security forces and destinations marked for apprehension. Wireless Sensor Networks (WSNs) are promising for border security surveillance because they enable enforcement teams to monitor events in the physical environment. In this work, probabilistic models have been presented to investigate senor development schemes while considering the environmental factors that affect the sensor performance. Simulation studies have been carried out using the OPNET to verify the theoretical analysis and to find an optimal node deployment scheme that is robust and efficient by incorporating geographical coordination in the design. Measures such as adding camera and range-extended antenna to each node have been investigated to improve the system performance. A prototype WSN based surveillance system has been developed to verify the proposed approach