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Hidden Markov Model for Visual Guidance of Robot Motion in Dynamic Environment
IEEE Transactions on Robotics and Automation
  • Qiuming Zhu, University of Nebraska at Omaha
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Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robot are presented. Characteristics that distinguish the visual computation and motion-control requirements in dynamic environments from that in static environments are discussed. Objectives of the vision and motion planning are formulated as: 1) finding a collision-free trajectory that takes account of any possible motions of obstacles in the local environment; 2) such a trajectory should be consistent with a global goal or plan of the motion; and 3) the robot should move at as high a speed as possible, subject to its kinematic constraints. A stochastic motion-control algorithm based on a hidden Markov model (HMM) is developed. Obstacle motion prediction applies a probabilistic evaluation scheme. Motion planning of the robot implements a trajectory-guided parallel-search strategy in accordance with the obstacle motion prediction models. The approach simplifies the control process of robot motion.

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Citation Information
Qiuming Zhu. "Hidden Markov Model for Visual Guidance of Robot Motion in Dynamic Environment" IEEE Transactions on Robotics and Automation Vol. 7 Iss. 3 (1991) p. 390 - 397
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