An unsupervised adaptive strategy for constructing probabilistic roadmaps

Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement. We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.

Authors

• Lydia Tapia
• Shawna L. Thomas
• Bryan Boyd
• Nancy M. Amato

Keywords

• Robots
• Strategic planning
• Libraries
• Unsupervised learning
• Machine learning
• Design automation
• Robotics and automation
• Computer science
• USA Councils
• Topology
• Unsupervised Adaptation
• Unsupervised Strategy
• Adaptive Method
• Space Planning
• Adaptation Planning
• User Intervention
• Wide Variety Of Environments
• Degrees Of Freedom
• Sampling Region
• Identification Of Regions
• Path Planning
• Homogeneous Regions
• Regional Environment
• Clear Clustering
• Problem Instances
• Drawback Of This Approach
• Collision Detection
• Training Manual
• Gaussian Sampling
• Uniform Random Sampling
• Low Visibility
• Fewer Nodes
• Free Region