This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

System Identification I

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Optimization techniques applied to multiple manipulators for path planning and torque minimization

There are several examples in natural systems that exhibit the self-organizing behavior of segregation when different types of units interact with each other. One of the best examples is a system of biological cells of heterogeneous types that has the ability to self-organize into specific formations, form different types of organs and, ultimately, develop into a living organism. Previous research in this area has indicated that such segregations in biological cells and tissues are made possible because of the differences in adhesivity between various types of cells or tissues. Inspired by this differential adhesivity model, this technical note presents a decentralized approach utilizing differential artificial potential to achieve the segregation behavior in a swarm of heterogeneous robotic agents. The method is based on the proposition that agents experience different magnitudes of potential while interacting with agents of different types. Stability analysis of the system with the proposed approach in the Lyapunov sense is carried out in this technical note. Extensive simulations and analytical investigations suggest that the proposed method would lead a population of two types of agents to a segregated configuration.

Segregation of Heterogeneous Units in a Swarm of Robotic Agents

The problem of perimeter detection and monitoring has a variety of applications. In this paper, a hybrid system of finite states is proposed for multiple autonomous robotic agents with the purpose of hazardous spill perimeter detection and tracking. In the system, each robotic agent is assumed to be in one of three states: searching, pursuing, and tracking. The agents are prioritized based on their states, and a potential field is constructed for agents in each state. For an agent in the tracking state, the agent's location and velocity as well as those of its closest leading and trailing agents are utilized to control its movement. The convergence of the tracking algorithm is analyzed for multiple spills under certain conditions. Simulation and experiment results show that with the proposed method, the agents can successfully detect and track the spills of various shapes, sizes, and movements.

Spill Detection and Perimeter Surveillance via Distributed Swarming Agents

One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious measurements from sensors must be identified and eliminated since their incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a unified sensor fusion strategy based on a modified Bayesian approach that can automatically identify the inconsistency in sensor measurements so that the spurious measurements can be eliminated from the data fusion process. The proposed method adds a term to the commonly used Bayesian formulation. This term is an estimate of the probability that the data is not spurious, based upon the measured data and the unknown value of the true state. In fusing two measurements, it has the effect of increasing the variance of the posterior distribution when measurement from one of the sensors is inconsistent with respect to the other. The increase or decrease in variance can be estimated using the information theoretic measure "entropy." The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data obtained from all sensors were fused simultaneously, and a decentralized or sequential Bayesian scheme that proved useful for identifying and eliminating spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious sensor measurements and eliminate them from the fusion process, thus leading to a better overall estimate of the true state. The proposed strategy was also validated with the help of experiments performed using stereo vision cameras, one infrared proximity sensor, and one laser proximity sensor. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace

A Method for Judicious Fusion of Inconsistent Multiple Sensor Data

The Army Research Office (ARO) has been supporting projects focusing on basic research in the area of smart materials and adaptive structures over recent years. A major emphasis of the ARO's Structures and Dynamics Program has been on the theoretical, computational, and experimental analysis of smart structures and structural dynamics, damping, active control, and health monitoring as applied to rotorcraft, electromagnetic antenna structures, missiles, land vehicles, and weapon systems. The variety of research projects supported by the program have been primarily directed towards improving the ability to predict, control, and optimize the dynamic response of complex, multi-body deformable structures. The projects in the field of smart materials and adaptive structures have included multi-disciplinary research conducted by teams of several faculty members as well as research performed by individual investigators. This paper begins with a brief discussion of smart or active materials, i.e. materials having capabilities of sensing changes from the surrounding environment and actively responding to those inputs in an effective manner. Integrating these materials in structures makes them `smart', i.e. it provides them with the capability to respond to the external stimuli to compensate for undesired effects and/or to enhance the desired effects. The terms `active', `smart', `adaptive', and `intelligent' are frequently used interchangeably in this context. This discussion is followed by illustrations from several current ARO-sponsored research projects related to smart materials and adaptive structures. A summary of significant results based upon these investigations is given next. Finally, directions of potential future research in the smart materials and adaptive structures area are discussed.

https://iopscience.iop.org/article/10.1088/0964-1726/10/4/304/pdf

Devendra P. Garg

Papers

Optimization techniques applied to multiple manipulators for path planning and torque minimization

Segregation of Heterogeneous Units in a Swarm of Robotic Agents

Spill Detection and Perimeter Surveillance via Distributed Swarming Agents

A Method for Judicious Fusion of Inconsistent Multiple Sensor Data

Current and potential future research activities in adaptive structures: an ARO perspective