Motion planning in the presence of moving obstacles
Journal of the ACM (JACM)
OBBTree: a hierarchical structure for rapid interference detection
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Robot Motion Planning
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
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Proceedings of the 2007 ACM symposium on Virtual reality software and technology
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Proceedings of the 2008 symposium on Interactive 3D graphics and games
A Robot Collision Avoidance Scheme Based on the Moving Obstacle Motion Prediction
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling
IEEE Transactions on Intelligent Transportation Systems
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This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational trade-offs.