PhD Thesis Proposal: Extending Probabilistic Roadmaps for Unknown Kinodynamic Constraints

Probabilistic Roadmap (PRM) planners have been used to generate paths for articulated robots for several years. By using random sampling techniques, PRM based planners are able to plot paths for robots with many degrees of freedom without needing to explore large parts of the search space that traditional planners would have to examine to create efficient paths. This has enabled them to be used with robots operating in high dimensional spaces, which are common in multi-agent robotics. Some limitations exist with PRM planners when they need to work with robots that are constrained in their motion or when several robots are involved in the plan. Current work has enabled planners with prior knowledge of agent constraints to predict how motion limitations will affect the robot in different poses. By adapting and extending PRM based algorithms to remove the need to know about constraints beforehand, the research proposed here aims to improve the capabilities of PRM methods and enable them to be used in more domains than is currently possible. Various algorithms using PRM are discussed in detail and new ideas on how some can be extended are outlined as well as describing the work that has already gone towards implementing such a system.