Human-Swarm Interactions via Coverage of Time-Varying Densities

نویسندگان

  • Yancy Diaz-Mercado
  • Sung G. Lee
  • Magnus Egerstedt
چکیده

One of the main challenges in human-swarm interactions is the construction of suitable abstractions that make an entire robot team amenable to human control. For such abstractions to be useful, they need to scale gracefully as the number of robots increases. In this work, we consider the use of time-varying density functions to externally influence a robot swarm. Density functions abstract away the size of the robot team and describe instead the concentration of agents over the domain of interest. This allows a human operator to design densities so as to manipulate the robot swarm as a whole, instead of at the individual robot level. We discuss coverage of time-varying density functions as a mechanism to translate densities into robotic movement, and provide a series of control laws that guarantee optimal coverage by the robot team. Distributed approximations allow the solutions to scale with the size of the robot team. This renders coverage a viable choice of method for influencing a robot swarm. Lastly, we provide a framework for the design of density functions that shape the swarm to achieve specified geometric configurations within the domain of interest. We show through robotic implementation in two different platforms the viability of human-swarm interactions with the proposed schemes.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Swarm Behavioral Inversion for Undirected Underwater Search

Coordinating the dynamics of large groups, or swarms, of autonomous underwater vehicles in order to search a given target area can be difficult due to the plurality of the system, environmental complications, and the prolonged and indefinite duration of the patrol. This paper examines the use of swarm inversion to optimize the behavioral dynamics of a swarm of autonomous agents in a patrol sear...

متن کامل

Numerical Solution of Optimal Control of Time-varying Singular Systems via Operational Matrices

In this paper, a numerical method for solving the constrained optimal control of time-varying singular systems with quadratic performance index is presented. Presented method is based on Bernste in polynomials. Operational matrices of integration, differentiation and product are introduced and utilized to reduce the optimal control of time-varying singular problems to the solution of algebraic ...

متن کامل

An Improved Particle Swarm Optimizer Based on a Novel Class of Fast and Efficient Learning Factors Strategies

The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and ex...

متن کامل

An Adaptive Particle Swarm Optimization for the Coverage of Wireless Sensor Network

The coverage problem is a crucial issue in wireless sensor networks (WSN); however, a high coverage rate ensures a high quality of service in WSN. This paper presents control of the coverage problem optimization via the adaptive particle swarm optimization (APSO) approach. The proper selection of inertia weight of APSO gives balance between global and local searching, and the research of this p...

متن کامل

A Robust Adaptive Observer-Based Time Varying Fault Estimation

This paper presents a new observer design methodology for a time varying actuator fault estimation. A new linear matrix inequality (LMI) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (FAFE). The FAFE is capable of estimating a wide range of time-varying actuator fault...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016