A probability-based approach to model-based path planning

By capitalizing on the known properties of harmonic potential functions this work develops a new approach to probability-based path planning that is intuitive, free from local traps (local minima) and computationally less complex than many existing methods. Although the approach presented here is based on the hill-climbing method, it is still able to guarantee goal attainment. Furthermore the algorithm presented here is able to handle arbitrary-shaped geometries and does not require any geometrical or topological approximation at the environment representation level.

Authors

• Iraj Mantegh
• Michael Jenkin
• Andrew A. Goldenberg

Keywords

• Path planning
• Orbital robotics
• Intelligent robots
• Collision avoidance
• Automation
• Laboratories
• Industrial engineering
• Computer science
• Mechanical factors
• Computational geometry
• Local Minima
• Goal Attainment
• Representation Of The Environment
• Harmonic Functions
• Time And Space
• Time Complexity
• Local Maxima
• Random Walk
• Far-field
• Repulsive Forces
• Space Complexity
• Visibility Graph
• Tree Search
• Obstacle Avoidance
• Directional Derivative
• Laplace Equation
• Network Path
• Combined Probability
• Field Boundaries
• Navigation Function
• Global Path
• Hill-climbing Algorithm
• Destination Point
• Effects Of Obstacles
• Global Plan