Sampling-based Motion Planning in Configuration and State Spaces: Using supervised learning tools

Robotic systems are the workhorses in practically all automated applications. Manufacturing industries, warehouses, elderly care, disaster rescue and (unfortunately) warfare are example applications where human life has benefited from robotics. By precisely planning controlling their motions via computer programs, real world tasks can be performed with high levels of accuracy and repeatability. Devising methods and algorithms that generate such motions by a) correctly reporting and finding the desired motion if it exists and b) doing so as fast as possible, has constituted the field of robot motion planning research over the last four decades.<br/><br/>In recent years, the Industry 4.0 initiative has provided a promising avenue for further advances in industrial automation. Modular, quickly reconfigurable and versatile robotic systems that safely collaborate with humans hold the key to future industrial automation. This is a challenging endeavor from an industrial and an academic perspective and inspires the work in this thesis. In alignment with these perspectives, this thesis is presented in two parts.<br/><br/>In the first part, we propose methods and frameworks to effectively utilize open source implementations of configuration space planners to realize flexible and robust solutions for bin picking. To this end, three results are presented: a tool to automatically tune parameters of path planning algorithm implementations, a world championship winning solution for industrial bin picking and a reactive collision avoidance framework for collaborative robotic applications.<br/><br/>Configuration space planners are extremely popular due to their solution speeds of about a tenth of a second for planning problems in 7-8 dimensions. However, a primary limitation of configuration space planners is that their planning solutions do not account for the physical laws governing the movement of robots. Consequently, the possibilities of generating versatile and dynamically feasible motions are highly curtailed. This limitation can be addressed by planning in the state space. However, sampling-based planning in state space is computationally intensive and challenging to realize in practice.<br/><br/>This challenge inspires the second part of this thesis. Here, the goal is to answer the question: Is it possible to achieve planning speeds in state space that are comparable to planning speeds in the configuration space? We pursue this goal by considering the Rapidly exploring Random Tree (RRT) planner in state space to plan a swing-up motion for a simple pendulum. Here, we propose two contributions that alleviate the computational demands of two critical steps in the RRT planner. We present a framework to approximate the distance (pseudo) metric and the steering function in state space using supervised learning tools. Together, a speed up of about 4 orders of magnitude is achieved relative to numerically solving for these two critical steps. However, reaching planning times equivalent to or better than what is achievable in configuration space still remains an elusive goal. Nevertheless, the achieved results serve as encouraging signs to pursue further research in this direction.