Multi-Robot Search for a Moving Target: Integrating World Modeling, Task Assignment and Context

Abstract

In this paper, we address coordination within a team of cooperative autonomous robots that need to accomplish a common goal. Our survey of the vast literature on the subject highlights two directions to further improve the performance of a multi-robot team. In particular, in a dynamic environment, coordination needs to be adapted to the different situations at hand (for example, when there is a dramatic loss of performance due to unreliable communication network). To this end, we contribute a novel approach for coordinating robots. Such an approach allows a robotic team to exploit environmental knowledge to adapt to various circumstances encountered, enhancing its overall performance. This result is achieved by dynamically adapting the underlying task assignment and distributed world representation, based on the current state of the environment. We demonstrate the effectiveness of our coordination system by applying it to the problem of locating a moving, non-adversarial target. In particular, we report on experiments carried out with a team of humanoid robots in a soccer scenario and a team of mobile bases in an office environment.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2016
Accession Number
AD1044140

Entities

People

  • Daniele Nardi
  • Emanuele Borzi
  • Francesco Riccio
  • Guglielmo Gemignani

Organizations

  • Sapienza University of Rome

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Case Studies
  • Environment
  • Literature
  • Management Engineering
  • Moving Targets
  • Multiagent Systems
  • Networks
  • Probability
  • Random Walk
  • Robotic Swarms
  • Robots
  • Sequential Monte Carlo Methods
  • Simulations
  • Simulators
  • Targets

Fields of Study

  • Computer science

Readers

  • Economics
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Autonomous System Control