Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation

Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robots 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...

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Bibliographic Details
Published inIEEE transactions on robotics and automation Vol. 7; no. 3; pp. 390 - 397
Main Author Zhu, Q.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.06.1991
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Hidden Markov models
Kinematics
Mobile robots
Motion control
Motion planning
Parallel robots
Robot control
Robotics
Stochastic processes
Strategic planning
Trajectory
Computer vision
Navigation
Moving robot
Markov model
Planning
Trajectory
Collision avoidance
Robotics
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ISSN1042-296X
DOI10.1109/70.88149

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Summary:Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robots 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, such as finding a collision-free trajectory that takes account of any possible motions of obstacles in the local environments. Such a trajectory should be consistent with a global goal or plan of the motion and 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 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.< >
ISSN:1042-296X
DOI:10.1109/70.88149