A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Architectural Considerations for Single Operator Management of Multiple Unmanned Aerial Vehicles

Recently, small Unmanned Aircraft Systems (UAS) have become ubiquitous in military battlefield operations due to their intelligence collection capabilities. However, these unmanned systems consistently demonstrate limitations and shortfalls with respect to size, weight, range, line of sight and information management. The United States Air Force Unmanned Aircraft Systems Flight Plan 2009-2047 describes an action plan for improved UAS employment which calls out single operator, multi-vehicle mission configurations. This thesis analyzes the information architecture using future concepts of operations, such as biologically-inspired flocking mechanisms. The analysis and empirical results present insight into the engineering of single-operator multiple-vehicle architectures

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

Formation Control of Nonholonomic Multi-Agent Systems

This dissertation is concerned with the formation control problem of multiple agents modeled as nonholonomic wheeled mobile robots. Both kinematic and dynamic robot models are considered. Solutions are presented for a class of formation problems that include formation, maneuvering, and flocking. Graph theory and nonlinear systems theory are the key tools used in the design and stability analysis of the proposed control schemes. Simulation and/or experimental results are presented to illustrate the performance of the controllers. In the first part, we present a leader-follower type solution to the formation maneuvering problem. The solution is based on the graph that models the coordination among the robots being a spanning tree. Our control law incorporates two types of position errors: individual tracking errors and coordination errors for leader-follower pairs in the spanning tree. The control ensures that the robots globally acquire a given planar formation while the formation as a whole globally tracks a desired trajectory, both with uniformly ultimately bounded errors. The control law is first designed at the kinematic level and then extended to the dynamic level. In the latter, we consider that parametric uncertainty exists in the equations of motion. These uncertainties are accounted for by employing an adaptive control scheme. In the second part, we design a distance-based control scheme for the flocking of the nonholonomic agents under the assumption that the desired flocking velocity is known to all agents. The control law is designed at the kinematic level and is based on the rigidity properties of the graph modeling the sensing/control interactions among the robots. A simple input transformation is used to facilitate the control design by converting the nonholonomic model into the single-integrator equation. The resulting control ensures exponential convergence to the desired formation while the formation maneuvers according to a desired, time-varying translational velocity. In the third part, we extend the previous flocking control framework to the case where only a subset of the agents know the desired flocking velocity. The resulting controllers include distributed observers to estimate the unknown quantities. The theory of interconnected systems is used to analyze the stability of the observer-controller system

Multiagent Systems for 3D Reconstruction Applications

3D models of scenes are used in many areas ranging from cultural heritage to video games. In order to model a scene, there are several techniques. One of the well-known and well-used techniques is image-based reconstruction. An image-based reconstruction starts with data acquisition step and ends with 3D model of the scene. Data are collected from the scene using various ways. The chapter explains how data acquisition step can be handled using a multiagent system. The explanation is provided by literature reviews and a study whose purpose is reconstructing an area in 3D using a multiagent UAV system

Multi-robot behaviors with bearing-only sensors and scale-free coordinates

This thesis presents a low-cost multi-robot system for large populations of robots, a new coordinate system for the robot based on angles between robots and a series of experiments validating robot performance. The new robot platform, the r-one will serve as an educational, outreach and research platform for robotics. I consider the robot's bearing-only sensor model, where each robot is capable of measuring the bearing, but not the distance, to each of its neighbors. This work also includes behaviors demonstrating the efficiency of this approach with this bearing-only sensor model. The new local coordinate systems based on angular information is introduced as scale-free coordinate system . Each robot produces its own local scale-free coordinates to determine the relative positions of its neighbors up to an unknown scaling factor. The computation of scale-free coordinates is analyzed with hardware and simulation validation. For hardware, the scale-free algorithm is tailored to low-cost systems with limited communication bandwidth and sensor resolution. The algorithm also uses a noise sensitivity model to reduce the impact of noise on the computed scale-free coordinates. I validate the algorithm with static and dynamic motion experiments

Cybernetic automata: An approach for the realization of economical cognition for multi-robot systems

The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays

Human Interaction with Robot Swarms: A Survey

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human–swarm interaction (HSI) and identifies the core concepts needed to design a human–swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human–swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works

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