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Moving Object Prediction for Off-road Autonomous Navigation



Rajmohan Madhavan, Craig I. Schlenoff


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


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


Madhavan, R. and Schlenoff, C. (2003), Moving Object Prediction for Off-road Autonomous Navigation, Proceedings of the SPIE Aerosense Conference, Orlando, FL, [online], (Accessed May 21, 2024)


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Created April 25, 2003, Updated February 17, 2017