Autonomous Robots

, Volume 41, Issue 1, pp 231–241 | Cite as

Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots

Article

Abstract

This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras.

Keywords

Cooperative control Target detection Path planning 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.The MITRE CorporationMcCleanUSA
  2. 2.Department of Aerospace Engineering and Institute for Systems ResearchUniversity of MarylandCollege ParkUSA
  3. 3.Naval Research Laboratory, WashingtonWashingtonUSA

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