Moving Object Prediction for Off-road Autonomous Navigation

Published

April 25, 2003

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

Abstract

The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured environment, the UGVs should have the difficult and computationally intensive competency to predict the future locations of moving objects that could interfere with its path. This paper details the development of a combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure. The development of a moving object testbed that facilitates the testing of different representations and prediction algorithms in an implementation-independent platform is also outlined.

Proceedings Title

Proceedings of the SPIE Aerosense Conference

Conference Dates

April 21-25, 2003

Conference Location

Orlando, FL

Conference Title

SPIE Aerosense Conference

Pub Type

Conferences

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Keywords

autonomous navigation, estimation theory., moving object prediction, path planning, uncertainty measure

Citation

Madhavan, R. and Schlenoff, C. (2003), Moving Object Prediction for Off-road Autonomous Navigation, Proceedings of the SPIE Aerosense Conference, Orlando, FL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822578 (Accessed October 12, 2025)