Principle Investigator: Lynton Dicks Supervisor: Karen Bradshaw CO-OPERATIVE MAPPING AND LOCALIZATION OF AUTONOMOUS ROBOTS

Introduction SLAM CSLAM History and Background Hardware Localization Algorithms Map Merging PRESENTATION OVERVIEW

Simultaneous Localization and Mapping (SLAM) Well researched for use on a single robot Uses: Google Autonomous Vehicles Navigate and map unreachable areas Military Reconnaissance Co-operative Mapping and Localization (CSLAM) Relatively new field Benefits: Team work saves time Improved Accuracy INTRODUCTION

SIMULTANEOUS LOCALIZATION AND MAPPING SLAMState Update Landmark Tracking (Dead reckoning) Landmark Extraction Data Association Pose TrackingOdometry

Each robots role Master-slave Independent Entities Centralization / Convergence Aggregation Communication methods COOPERATIVE MAPPING AND LOCALIZATION

Generic Framework for both online and offline SLAM Implemented SLAM for use with one robot Generic Programming Framework to combine standard robotic operations with AI Abstracts away the details of interfacing and controlling robots Easy to implement new robot hardware classes to allow the framework to work with new hardware HISTORY AND BACKGROUND Autonomous Robotic Programming Framework – Leslie Luyt 2009 A Robotic Framework for use in Simultaneous Localization and Mapping Algorithms – Shaun Egan 2010

Two Encoder Motors Two Ultrasonic Sensors A Bluetooth Controller – 10m range, ability to keep several connections alive at the same time HARDWARE – FISCHERTECHNIK ROBOT

HARDWARE: ADDONS Motor EncodersUltrasonic Sensors

TRIANGULAR BASED FUSION

LOCALIZATION ALGORITHMS Constraints: Unique Landmark Associations and adequately spaced landmarks Time between observations Static Environment Limited to two robots The Algorithms Extended Kalman Filter Monte Carlo Particle Filter

MAP MERGING Merge maps with observed robot Maps are transformed (rotated, translated) through merging algorithm Merging maps of populated environments by keeping track of moving objects