Improved roadmap connection via local learning for sampling based planners

Probabilistic Roadmap Methods (PRMs) solve the motion planing problem by constructing a roadmap (or graph) that models the motion space when feasible local motions exist. PRMs and variants contain several phases during roadmap generation i. e. , sampling, connection, and query. Some work has been done to apply machine learning to the connection phase to decide which variant to employ, but it uses a global learning approach that is inefficient in heterogeneous situations. We present an algorithm that instead uses local learning: it only considers the performance history in the vicinity of the current connection attempt and uses this information to select good candidates for connection. It thus removes any need to explicitly partition the environment which is burdensome and typically difficult to do. Our results show that our method learns and adapts in heterogeneous environments, including a KUKA youBot with a fixed and mobile base. It finds solution paths faster for single and multi-query scenarios and builds roadmaps with better coverage and connectivity given a fixed amount of time in a wide variety of input problems. In all cases, our method outperforms the previous adaptive connection method and is comparable or better than the best individual method.

Authors

• Chinwe Ekenna
• Diane Uwacu
• Shawna L. Thomas
• Nancy M. Amato

Keywords

• Planning
• Robots
• Sampling methods
• Euclidean distance
• History
• Data structures
• Local Learning
• Path Planning
• Variety Of Problems
• Global Learning
• Functional Changes
• Degrees Of Freedom
• Sampling Method
• Estimation Method
• Urban Planning
• Spatial Memory
• Distance Metrics
• 3D Environment
• Narrow Region
• Nearest Neighbor Search
• Poor Choices
• Bottom Left
• Adaptive Sampling
• Multiple Robots
• Single Query
• Percentage Of Nodes
• Rigid Robots
• Instance-based Learning