Accession Number:
ADA411839
Title:
Perception-based Co-evolutionary Reinforcement Learning for UAV Sensor Allocation
Descriptive Note:
Phase 1 of Final rept. 1 Jul 2002-3 Feb 2003
Corporate Author:
INTELLIGENT INFERENCE SYSTEMS CORP SUNNYVALE CA
Personal Author(s):
Report Date:
2003-02-01
Pagination or Media Count:
15.0
Abstract:
In this project, we have formulated the problem of sensor allocation in a team of UAVs within a mathematical programming framework. A Perception-based reasoning approach based on co-evolutionary reinforcement learning was developed for jointly addressing sensor allocation on each individual UAV and allocation of a team of UAVs in the geographical search space. An elaborate problem setup was simulated and experimented with, for testing and analysis of this framework using the Player-Stage multi-agent simulator. This simulator was developed jointly at the USC Robotics Research Lab and HRL Labs.The experimental results demonstrated a very strong performance of our methodology for UAV sensor allocation problem domains. Our results indicate that not only it is feasible to use perception-based reinforcement learning for this problem but it is an adequate solution for many typical UAV teams.
Descriptors:
Subject Categories:
- Miscellaneous Detection and Detectors