Machine Learning Approach Towards Motion Planning for Manipulation

Abstract

Motion planning is an essential part of robotics. This is important for robots to perform navigational and manipulative tasks. This aims at finding a path from a start position to a goal position while avoiding collisions with obstacles. These days robots are being deployed in public places where they need to deal with uncertainty and a dynamic environment. A motion planning problem generally requires a robot to deal with a dynamic environment, uncertainty, and kinodynamic (velocity, acceleration, and force/torque) constraints. A significant amount of work has been done to solve the problem of motion planning in a dynamic environment. Numerous ways to deal with movement arranging and obstacle avoidance algorithms have been proposed. Those methods can be categorized into sampling-based, imitation learning, and biological inspired, and deep learning. Those approaches have been improved techniques and algorithms for solving motion planning problems. Even though several new efficient techniques have been proposed and many existing ones have been improved, the multitude of motion planning issues has been steadily growing. These problems involve the determination of collision-free paths, modeling of changing environment, real-time recognition of obstacles, and dynamic constraints, etc. These limitations create movement arranging issues really testing and require solid and effective calculations, procedures, and approaches. This study attempts to introduce a deep learning-based planner to Kautham which is a motion planning simulation tool for study and research purposes. Kautham uses OMPL which offers sampling-based planners. Kautham relies on these planners, so it also inherits the problems of sampling-based algorithms. We hope that a deep learning-based planner can reduce the computation time for various environmental settings and can improve Kautham performance and provide a chance to researchers and students to learn and understand deep learning planners. This work results shows that machine learning based planner computation time is less than the sampling based planner.