Benchmark on robotic motion planning algorithms in a drilling task - Lim, Joyce Xin Yan (2018)
Sampling-based robotic motion planning algorithms have been the key concept of programming robot motion as they are validated to be probabilistically complete. Thus, numerous research studies have been carried out to continuously improve these algorithms to lower the cost, which includes the time taken to solve the query or the trajectory execution time. This report covers the benchmark of sampling-based motion planners, such as single-query and multi-query planners, in a static environment to determine whether reusing the roadmap of multi-query planners will reduce the cost of solving multiple queries, as compared to single-query planners where new trees are constructed for every query. The benchmark requires the use of Open Robotics Automation Virtual Environment (OpenRAVE), which provides an environment to simulate robot motion with their planners, and Open Motion Planning Library (OMPL) which has a variety of motion planners. The study also includes the integration of OMPL into OpenRAVE since the benchmark is carried out by using planners from not only OpenRAVE, but OMPL as well. Specifically, the benchmark will cover the total time taken to solve the queries with smoothing, total trajectory execution time and robustness of the planners. Also, the programming language used in this project is Python.