Information Driven Coordinated Air-Ground Proactive Sensing

Abstract

ó 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 de- veloped for identication 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 congurations. Comple- mentary 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 efciently realize such collaborative potential. I. INTRODUCTION The use of robots in surveillance and exploration is gaining prominence. Typical applications include air and ground based mapping of predetermined areas for tasks such as surveillance, target detection, tracking, and search and rescue operations. The use of multiple collaborative robots is ideally suited for such tasks. A major thrust within this area is the optimal control and use of robotic resources so as to reliably achieve the goal at hand. This paper addresses this very problem of coordinated deployment of robotic sensor platforms. Consider the task of reliably detecting and localizing an unknown number of features within a prescribed search area. In this setting, it is highly desired to fuse information from all available sources. It is also benecial to proactively focus the attention of resources minimizing the uncertainty in detection and localization. Deploying teams of robots working towards this common objective offers several advantages. Large envi- ronments preclude the option for complete sensor coverage. Attempting to increase coverage leads to trade-offs between resolution or accuracy and computational constraints in terms of required storage and processing. A scalable and e xible solution is therefore desirable. Efforts are directed toward commonly available and rel- atively low cost sensors and robotic vehicles. The primary sensor considered are consumer grade digital cameras. These may be x ed or carried on-board a sensor platform. Available sensor platforms include x ed wing unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs) derived from commercial model kits. Intuition suggests aerial and ground vehicles exhibit com- plementary capabilities and characteristics as robotic sensor platforms. Fixed wing aircraft offer broad eld of view and rapid coverage of search areas. However, minimum limits on operating airspeed and altitude, combined with attitude uncertainty, place a lower limit on their ability to resolve and localize ground features. Ground vehicles on the other hand offer high resolution sensing over relatively short ranges with the disadvantage of obscured views and slow coverage. This proposition will be tested through analysis of measurement uncertainty for the basic vision sensor and the combined characteristics once installed on air and ground platforms. Simply bringing together a group of sensing resources does not necessarily ensure their potential is obtained. Careful de- cisions are required to establish system estimation and control architectures that achieve desired performance and scalability. This paper presents a decentralized architecture and solution methodology for seamlessly and transparently realizing the