Information Driven Coordinated Air-Ground Proactive Sensing

This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground robotic sensor platforms. The approach taken builds on well known Decentralized Data Fusion (DDF) methodology. In particular, it brings together established representations developed for identification and linearized estimation problems to jointly address feature detection and localization. This provides transparent and scalable integration of sensor information from air and ground platforms. As in previous studies, an Information-theoretic utility measure and local control strategy drive the robots to uncertainty reducing team configurations. Complementary characteristics in terms of coverage and accuracy are revealed through analysis of the observation uncertainty for air and ground on-board cameras. Implementation results for a detection and localization example indicate the ability of this approach to scalably and efficiently realize such collaborative potential.

Authors

• Ben Grocholsky
• Rahul Swaminathan
• James Keller 0002
• Vijay Kumar 0001
• George J. Pappas

Keywords

• Robot sensing systems
• Robot kinematics
• Sensor phenomena and characterization
• Land vehicles
• Control systems
• Collaboration
• Surveillance
• Unmanned aerial vehicles
• Laboratories
• Computer vision
• Control Strategy
• Local Control
• Estimation Problem
• Feature Detection
• Robotic Platform
• Complementary Features
• Sensor Platform
• Observational Uncertainty
• Ground Features
• Onboard Camera
• Robot Sensors
• Ground Robots
• Target Location
• Mutual Information
• Aerial Vehicles
• Detection Process
• Ground Plane
• Image Sensor
• Inertial Measurement Unit
• Unmanned Ground Vehicles
• Use Of Robots
• Ground Vehicles
• Air Vehicles
• Detection Uncertainty
• Intrinsic Matrix
• Primary Sensor
• Seamless Integration
• Search Area
• Ground Points