Conference Paper

OOPS for Motion Planning: An Online, Open-source, Programming System

Dept. of Comput. Sci., Rice Univ., Houston, TX
DOI: 10.1109/ROBOT.2007.364047 Conference: Robotics and Automation, 2007 IEEE International Conference on
Source: IEEE Xplore


The success of sampling-based motion planners has resulted in a plethora of methods for improving planning components, such as sampling and connection strategies, local planners and collision checking primitives. Although this rapid progress indicates the importance of the motion planning problem and the maturity of the field, it also makes the evaluation of new methods time consuming. We propose that a systems approach is needed for the development and the experimental validation of new motion planners and/or components in existing motion planners. In this paper, we present the online, open-source, programming system for motion planning (OOPSMP), a programming infrastructure that provides implementations of various existing algorithms in a modular, object-oriented fashion that is easily extendible. The system is open-source, since a community-based effort better facilitates the development of a common infrastructure and is less prone to errors. We hope that researchers will contribute their optimized implementations of their methods and thus improve the quality of the code available for use. A dynamic Web interface and a dynamic linking architecture at the programming level allows users to easily add new planning components, algorithms, benchmarks, and experiment with different parameters. The system allows the direct comparison of new contributions with existing approaches on the same hardware and programming infrastructure

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    • "This choice was made for efficiency reasons. With additional levels of abstraction in the implementation it would have been possible to avoid separate implementations [20]. The downside would have been that the implementation of planners would have had to follow a strict structure, which makes the implementation of new algorithms more difficult and possibly less efficient. "
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    ABSTRACT: The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).
    IEEE Robotics &amp amp amp Automation Magazine 12/2012; 19(4):72-82. DOI:10.1109/MRA.2012.2205651 · 2.41 Impact Factor
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    • "SSV is the most well-known bounding volume for distance computation [16] for complicated polygonal models, which was implemented in PQP library [1]. As a result, PQP and its variants have been used in many motion planners such as MPK [17], OOPSMP [18], OMPL [19] and ROS [20]. SSV utilizes three types of bounding volume shapes: point swept sphere (PSS), line swept sphere (LSS) and rectangle swept sphere (RSS), to provide varying tightness for the underlying objects. "
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    ABSTRACT: We present a new bounding volume structure, k-IOS that is an intersection of k spheres, for accelerating proximity query including collision detection and Euclidean distance computation between arbitrary polygon-soup models that undergo rigid motion. Our new bounding volume is easy to implement and highly efficient both for its construction and runtime query. In our experiments, we have observed up to 4.0 times performance improvement of proximity query compared to an existing well-known algorithm based on swept sphere volume (SSV) [1]. Moreover, k-IOS is strictly convex that can guarantee a continuous gradient of distance function with respect to object's configuration parameter.
    Proceedings - IEEE International Conference on Robotics and Automation 01/2012; DOI:10.1109/ICRA.2012.6224889
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    • "We have implemented the C-PRM algorithm and C-PRM with path hybridization within the framework of the OOPSMP motion planning package [4]. Our implementation supports the combination of a wide range of path quality criteria (length, smoothness, clearance, as well as the number of reverse car motions). "
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    ABSTRACT: Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths, with respect to different quality measures such as path length, clearance, smoothness or energy, is often notoriously low. This problem is accentuated in the case of non-holonomic sampling-based motion planning, in which the space of feasible motion trajectories is restricted. In this study, we combine the C-PRM algorithm by Song and Amato with our recently introduced path-hybridization approach, for creating high quality non-holonomic motion paths, with combinations of several different quality measures such as path length, smoothness or clearance, as well as the number of reverse car motions. Our implementation includes a variety of code optimizations that result in nearly real-time performance, and which we believe can be extended with further optimizations to a real-time tool for the planning of high-quality car-like motion. Comment: 2 pages
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