Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings

Multi-Objective UAV Mission Planning Using Evolutionary Computation

This investigation purports to develop a new model for multiple autonomous aircraft mission routing. Previous research both related and unrelated to this endeavor have used classic combinatoric problems as models for Unmanned Aerial Vehicle (UAV) routing and mission planning. This document presents the concept of the Swarm Routing Problem (SRP) as a new combinatorics problem for use in modeling UAV swarm routing, developed as a variant of the Vehicle Routing Problem with Time Windows (VRPTW). The SRP removes the single vehicle per target restraint and changes the customer satisfaction requirement to one of vehicle on location volume. The impact of these alterations changes the vehicle definitions within the problem model from discrete units to cooperative members within a swarm. This represents a more realistic model for multi-agent routing as a real world mission plan would require the use of all airborne assets across multiple targets, without constraining a single vehicle to a single target. Solutions to the SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to the development of a multi-objective evolutionary algorithm. This algorithm design is implemented using C++ and an evolutionary algorithm library called Open Beagle. Benchmark problems applied to the VRPTW show the usefulness of this solution approach. A full problem definition of the SRP as well as a multi-objective formulation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. These solutions show the SRP solution is comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution

GUARDIANS final report part 1 (draft): a robot swarm assisting a human fire fighter

Emergencies in industrial warehouses are a major concern for fire fighters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist re ghters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting re ghters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the fire fighters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Algorithms based on spider daddy long legs for finding the optimal route in securing mobile ad hoc networks

Mobile ad hoc networks (MANETs) are wireless networks that are subject to severe attacks, such as the black hole attack. One of the goals in the research is to find a method to prevent black hole attacks without decreasing network throughput or increasing routing overhead. The routing mechanism in define uses route requests (RREQs; for discovering routes) and route replies (RREPs; for receiving paths). However, this mechanism is vulnerable to attacks by malicious black hole nodes. The mechanism is developed to find the shortest secure path and to reduce overhead using the information that is available in the routing tables as an input to propose a more complex nature-inspired algorithm. The new method is called the Daddy Long-Legs Algorithm (PGO-DLLA), which modifies the standard AODV and optimizes the routing process. This method avoids dependency exclusively on the hop counts and destination sequence numbers (DSNs) that are exploited by malicious nodes in the standard AODV protocol. The experiment by performance metrics End-to-End delay and packet delivery ratio are compared in order to determine the best effort traffic. The results showed the PGO-DLLA improvement of the shortest and secure routing from black hole attack in MANET. In addition, the results indicate better performance than the related works algorithm with respect to all metrics excluding throughput which AntNet is best in routing when the pause time be more than 40 seconds. PGODLLA is able to improve the route discovery against the black hole attacks in AODV. Experiments in this thesis have shown that PGO-DLLA is able to reduce the normalized routing load, end-to-end delay, and packet loss and has a good throughput and packet delivery ratio when compared with the standard AODV protocol, BAODV protocol, and the current related protocols that enhance the routing security of the AODV protocols

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