Multi-Agent Based Simulation of an Unmanned Aerial Vehicles System

The rapid growth of using Unmanned Aerial Vehicles (UAV) for civilian and military applications has promoted the development of research in many areas. Most of the unmanned aerial vehicles in use are manually controlled. Often, UAVs require highly trained pilot operators. Hence, the main challenge faced by researchers has been to make UAVs autonomous or semiautonomous. The goal of this research project is to develop and implement a simulation for a user-defined environment allowing UAVs to maneuver in free environments and obstacle-laden environments using Boid's algorithm of flocking with obstacle avoidance. The users are permitted to analyze the maneuvering area and coverage efficiency of the UAVs and to dynamically change environments. This project makes use of Boid's flocking algorithm to generate different kinds of movements for the flying agents, enabling the user to analyze the effectiveness of patrolling in that particular scenario. The number of UAVs and the type of environment are set by the user. The set number of UAVs moves as a flock or swarm inside the set environment by using Boid's rules of flocking: cohesion, alignment, and separation. The coverage efficiency of the UAVs in that particular environment is reported based on the ratio between the area covered and the time when the search time reaches a threshold. The advantages and feasibilities of the approach are discussed with the simulation results

Distributed tracking control of leader-follower multi-agent systems under noisy measurement

In this paper, a distributed tracking control scheme with distributed estimators has been developed for a leader-follower multi-agent system with measurement noises and directed interconnection topology. It is supposed that each follower can only measure relative positions of its neighbors in a noisy environment, including the relative position of the second-order active leader. A neighbor-based tracking protocol together with distributed estimators is designed based on a novel velocity decomposition technique. It is shown that the closed loop tracking control system is stochastically stable in mean square and the estimation errors converge to zero in mean square as well. A simulation example is finally given to illustrate the performance of the proposed control scheme.Comment: 8 Pages, 3 figure

Toward A Mobile Agent Relay Network

Although wireless communication provides connectivity where hardwired links are difficult or impractical, it is still hindered by the environmental conditions where the communicators reside. Signal loss over large distances or because of intervening obstacles can be mitigated by increasing the user\u27s transmission power or adding repeater nodes between the users. Unfortunately, increasing the signal strength strains limited power resources and increases the likelihood of eavesdropping. Stationary repeaters are impractical for highly mobile users in dangerous environments. While mobile relay nodes might be a preferred solution, a centralized control scheme saps bandwidth from important traffic and introduces a single point of failure at the control station. An alternative solution is to create a Mobile Agent Relay Network (MARN). Each autonomous node in the MARN decides where to move to maintain the network connectivity using only locally-available information from onboard sensors and communication with in-range neighbor nodes. This is achieved by borrowing concepts from flocking behaviors that motivates our agents to maintain equal distance between its neighboring nodes. In addition, each agent maintains a filtered list of previously visited locations that provided best connection. This thesis takes the first steps toward realizing a MARN by providing mobile relay agents. Each model-based reflex agent is guided by a modified flocking behavior which considers only trustworthy neighbors and uses a Bayesian model to aggregate observations and shared reputation. The relay agents are able to build a network and maintain connectivity for their users. In this work, MARN agent algorithms are evaluated in a simulated unobstructed environment with stationary users. The system behavior is explored under both benign conditions and with varying numbers of misbehaving nodes

SwarMAV: A Swarm of Miniature Aerial Vehicles

As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible avenue of development: an autonomous swarm of MAVs which exploits its inherent reliability (through redundancy), and its ability to exchange information among the members, in order to cope with a dynamically changing environment and achieve its mission. We describe the successful realization of a prototype experimental platform weighing only 75g, and outline a strategy for the automatic design of a suitable controller

Mobile Sensing Cluster Based on Swarm Intelligence with Multiple Autonomous Mobile Devices

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Starling flock networks manage uncertainty in consensus at low cost

Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.Comment: 19 pages, 3 figures, 9 supporting figure

A Unified Analytical Look at Reynolds Flocking Rules

In this paper, we present a unified theoretical view of the so-called ``Flocking Rules of Reynolds'' introduced in 1987. No equations describing the rules or mathematical models of the mobile agents known as ``boids'' were presented in the original work by Reynolds. We show how to model a group of autonomous mobile agents by dynamic nets and achieve flocking by dissipation of the structural energy of the multi-agent system. As a by-product, we obtain a single protocol called the (alpha,alpha) protocol that encompasses all three flocking rules of Reynolds. We provide geometric interpretations of the advanced forms of some of these flocking rules. Simulation results are provided that demonstrate flocking of 100 agents towards a sink