Multi agents' multi targets mission under uncertainty using probability navigation function

Shlomi Hacohen, Shraga Shoval, Nir Shvalb

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we consider the problem of cooperative control of a swarm of autonomous heterogeneous mobile agents that are required to intercept a group of moving targets while avoiding contacts with dynamic obstacles. Traditionally these type of problems are solved by decomposing the solution into several sub problems: targets assignments, coordinated interception control, motion planning and motion control. In this paper we present a simultaneous solution to these problems based on the Probabilistic Navigation Function (PNF). The proposed solution considers uncertainties in the targets and obstacles locations. such that the locations and geometries of the targets and obstacles are given by Gaussian probability distributions. These probabilities are convoluted with the agents', obstacles' and targets' geometries to provide a Global Probability Navigation Function - φ. The PNF provides an analytic solution, and guarantees a simultaneous interception of all targets while limiting the risk of the agents to a given value. The complexity of the solution is linear with the number of targets and agents, and therefore is not limited to small problems. Although the solution provided by the PNF is not optimal, it provides simple and efficient solution, making it suitable for a large range of real time applications.

Original languageEnglish
Title of host publication2017 13th IEEE International Conference on Control and Automation, ICCA 2017
PublisherIEEE Computer Society
Pages845-850
Number of pages6
ISBN (Electronic)9781538626795
DOIs
StatePublished - 4 Aug 2017
Event13th IEEE International Conference on Control and Automation, ICCA 2017 - Ohrid, Macedonia, The Former Yugoslav Republic of
Duration: 3 Jul 20176 Jul 2017

Publication series

NameIEEE International Conference on Control and Automation, ICCA

Conference

Conference13th IEEE International Conference on Control and Automation, ICCA 2017
Country/TerritoryMacedonia, The Former Yugoslav Republic of
CityOhrid
Period3/07/176/07/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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