Browsing by Author "Orthey, Andreas"

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    MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
    (IEEE, 2022) Chamzas, Constantinos; Quintero-Peña, Carlos; Kingston, Zachary; Orthey, Andreas; Rakita, Daniel; Gleicher, Michael; Toussaint, Marc; Kavraki, Lydia E.
    Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker , an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.
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    Sampling-Based Motion Planning: A Comparative Review
    (Annual Reviews, 2024) Orthey, Andreas; Chamzas, Constantinos; Kavraki, Lydia E.
    Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guide and reference manual for the use of sampling-based motion planning algorithms. It includes a history of motion planning, an overview of the most successful planners, and a discussion of their properties. It also shows how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks highlights their respective differences from sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems in order to provide insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field but also a guide for practitioners to make informed algorithmic decisions.