ITIRRT: A Decoupled Framework for the Integration of Machine Learning Into Path Planning

Abstract

Machine learning models have been successfully applied in many separate domains in the recent years. Yet, there is still a large disconnect with the path planning community mainly due to implementation difficulty. In this paper, we identify the issues and present a path planning framework that aims to ease the integration of machine learning models. The framework is divided into two phases: prediction and planning. During the prediction phase machine learning models are used to predict a path solution which, while not perfect, is enough to jump-start our proposed planner. The planning phase begins with the initialization of multiple independent tree data structures and, during execution, our planner merges the different trees until a path is found. Due to the architecture of our framework, the prediction and the planning phases are totally decoupled, resulting in a planner that treats the information given by the prediction model as a guess and not as a path to be corrected. This also allows our planner to maintain its probabilistic completeness. Our planner was evaluated on simulated environments and on a small robotic arm. We show that our planner can successfully leverage predicted paths to significantly improve the planning performances. We also demonstrate the flexibility of our framework by integrating multiple machine learning models of varying complexity.