Planning-based prediction for pedestrians
Top Cited Papers
- 1 October 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3931-3936
- https://doi.org/10.1109/iros.2009.5354147
Abstract
We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.Keywords
This publication has 10 references indexed in Scilit:
- Intentional motion on-line learning and predictionMachine Vision and Applications, 2008
- Safe motion planning in dynamic environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Apprenticeship learning via inverse reinforcement learningPublished by Association for Computing Machinery (ACM) ,2004
- A hierarchical, multi-resolutional moving object prediction approach for autonomous on-road drivingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Moving object prediction for off-road autonomous navigationPublished by SPIE-Intl Soc Optical Eng ,2003
- Predictive autonomous robot navigationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Learning motion patterns of persons for mobile service robotsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- On motion planning of mobile robots which coexist and cooperate with humanPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Learning Variable-Length Markov Models of BehaviorComputer Vision and Image Understanding, 2001
- A note on two problems in connexion with graphsNumerische Mathematik, 1959