SPATIAL DATA STRUCTURES FOR ROBOTIC VEHICLE ROUTE PLANNING

ADS IS PROPOSING TO DEVELOP OBJECT-ORIENTED, SCHEMA-BASED DATA STRUCTURES FOR REPRESENTING TERRAIN DATA. THESE DATA STRUCTURES CAN SATISFY DEMANDING SPACE AND TIME CONSTRAINTS BY MINIMIZING PAGING IN A VIRTUAL MEMORY SYSTEM. THIS WILL BE ACCOMPLISHED BY COMPACTLY REPRESENTING THE DATA IN A FORMAT DIRECTLY RELEVANT TO THE ROUTE PLANNING APPLICATIONS. THE SCHEMATA EXPLICITLY REPRESENT THRESS ASPECTS OF TERRAIN FEATURES: SPATIAL ATTRIBUTES, WHICH ARE BUILT UPON TWO-DIMENSIONAL, MULTI-RESOLUTION DESCRIPTIONS OF CURVES AND REGIONS AUGMENTED WITH ELEVATIONS AT FEATURE POINTS; NON-SPATIAL ATTRIBUTES; AND RELATIONS AMOND THE SCHEMATA (E.G., ADJACENCY). IMAGE ANALYSIS ROUTINES WILL BE USED TO EXTRACT TERRAIN FEATURES FROM THE RAW DATA. THE DATA STRUCTURES WILL BE TAILORED TO THE APPLICATIONS SO THAT PLANNING ALGORITHMS USING AI TECHNIQUES (E.G., GRAPH SEARCH) WILL HAVE READY ACCESS TO PERTINENT DATA. THE PHASE I EFFORT WILL USE EXISTING ADS TOOLS TO DESIGN AND IMPLEMENT A PARTIAL DATA BASE FOR REPRESENTATIVE FEATURES FROM A REAL SOURCE OF TERRAIN DATA. DUE TO THE MODULAR NATURE OF THE SCHEMATA THESE DATA STRUCTURES WILL BE COMPATIBLE WITH PHASE II GOALS FOR DYNAMIC UPDATES.