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Author

Ioan A. Sucan

Bio: Ioan A. Sucan is an academic researcher from Willow Garage. The author has contributed to research in topics: Motion planning & Mobile robot. The author has an hindex of 20, co-authored 31 publications receiving 2824 citations. Previous affiliations of Ioan A. Sucan include Rice University & International University, Cambodia.

Papers
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Journal ArticleDOI
TL;DR: The open motion planning library is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms, and it can be conveniently interfaced with other software components.
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).

1,472 citations

Journal ArticleDOI
TL;DR: MoveIt! will allow robots to build up a representation of their environment using data fused from three-dimensional (3-D) and other sensors, generate motion plans that effectively and safely move the robot around in the environment, and execute the motion plans while constantly monitoring the environment for changes.
Abstract: R obots are increasingly finding applications in domains where they have to work in close proximity to humans. Industrial robotic applications are starting to examine the possibility of robots and humans as coworkers, sharing tasks and workspace. Autonomous robotic cars operating on crowded streets and freeways have to share space with pedestrians and cyclists in addition to other vehicles. Domestic robots, in particular mobile manipulation systems, will be confronted with cluttered, messy environments where obstacles exist at every corner, and people are continuously moving in and out of the workspace of the robots. Robots working in human environments clearly have to be aware of their surroundings andmust actively attempt to avoid collisions with humans and other obstacles. MoveIt! is a set of software packages integrated with the Robot Operating System (ROS) and designed specifically to provide such capabilities, especially for mobile manipulation. MoveIt! will allow robots to build up a representation of their environment using data fused from three-dimensional (3-D) and other sensors, generate motion plans that effectively and safely move the robot around in the environment, and execute the motion plans while constantly monitoring the environment for changes.

382 citations

DOI
30 May 2014
TL;DR: The MoveIt! framework as discussed by the authors is an open-source tool for mobile manipulation in ROS that allows users to quickly get basic motion planning functionality with minimal initial setup, automate its configuration and optimization, and easily customize its components.
Abstract: Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users.

205 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors, aiming to address the uncertainty due to sensor and execution errors.
Abstract: We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).

204 citations

Book ChapterDOI
01 Jan 2009
TL;DR: Applications of motion planning have also expanded to fields such as graphics and computational biology, and the field that addresses planning for complex robots with kinematic and dynamic constraints is addressed.
Abstract: Over the last two decades, motion planning [4, 15, 17] has grown from a field that considered basic geometric problems to a field that addresses planning for complex robots with kinematic and dynamic constraints [5]. Applications of motion planning have also expanded to fields such as graphics and computational biology [16].

195 citations


Cited by
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Proceedings ArticleDOI
09 May 2011
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
Abstract: With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.

4,501 citations

Journal ArticleDOI
TL;DR: An open-source framework to generate volumetric 3D environment models based on octrees and uses probabilistic occupancy estimation that represents not only occupied space, but also free and unknown areas and an octree map compression method that keeps the 3D models compact.
Abstract: Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

2,135 citations

Journal ArticleDOI
TL;DR: The open motion planning library is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms, and it can be conveniently interfaced with other software components.
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).

1,472 citations

01 Jan 2013
TL;DR: In this paper, an open-source framework is presented to generate volumetric 3D environ- ment models based on octrees and uses probabilistic occupancy estimation, which explicitly repre- sents not only occupied space, but also free and unknown areas.
Abstract: Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environ- ment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly repre- sents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The re- sults demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

1,388 citations

Journal ArticleDOI
TL;DR: The dissertation presented in this article proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.
Abstract: Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks. The dissertation presented in this article (Rusu, PhD thesis, 2009) proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.

908 citations