Artificial Potential Biased Probabilistic Roadmap Method

Probabilistic roadmap methods (PRM) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.

Authors

• Daniel Aarno
• Danica Kragic
• Henrik I. Christensen

Keywords

• Sampling methods
• Path planning
• Orbital robotics
• Space technology
• Robots
• Distributed computing
• Laplace equations
• Artificial Potential
• Sampling Bias
• Free Space
• Regional Lymph Nodes
• Narrow Region
• Distribution Of Nodes
• Probable Path
• Artificial Potential Field
• Heuristic
• Local Minima
• Solution Of Equation
• Search Algorithm
• Grid Points
• Finite Difference Method
• Points In Region
• Configuration Space
• Laplace Equation
• Dijkstra’s Algorithm
• Random Sampling Strategy
• Concave Regions
• Solution Path
• Task Planning
• Collision Detection