The Open Motion Planning Library

By Ioan Alexandru Sucan, Mark Moll, Lydia E. Kavraki. 2012

1,903 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Reinforcement Learning

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Citations in the corpus

Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “[18]) them in existing path planners (such as [29, 35]) a priori.”
Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, methodology): “0 includes an integration with the popular Open Motion Planning Library (OMPL), giving access to a suite of motion planning algorithms [66].”
RLBench: The Robot Learning Benchmark & Learning Environment (2019, methodology): “RLBench, through the task building tool mentioned in Section IV-D, provides expert algorithm π∗ for each different task and their corresponding variations, allowing for demonstration episodes to be generated The episodes produced via π∗ come from using the Open Motion Planning Library [42].”
FCL: A general purpose library for collision and proximity queries (2012, methodology): “We integrate our collision checking methods with the open source OMPL motion planning library [2] and demonstrate fast and accurate motion planning that allows the robot to complete its task.”
Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners (2019, methodology): “library (OMPL) [60] implementations for classical planners.”
Learning preferences for manipulation tasks from online coactive feedback (2015, background): “This makes it infeasible to manually encode them in existing path planners (e.g., Zucker et al. [63], Sucan et al. [57], Schulman et al. [50]) a priori.”
Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs (2014, methodology): “BIT* was tested against existing algorithms in both simulated random worlds (Section V-A) and real-world manipulation problems (Section V-B) using publicly available Open Motion Planning Library (OMPL) [38] implementations.”
CommonRoad: Composable benchmarks for motion planning on roads (2017, background): “…but the data has never been or is no longer publicly available, e.g. [13]–[15]. c) Motion planning libraries: One of the most successful motion planning libraries in robotics is the Open Motion Planning Library (OMPL) [16], which implements many of the most important sampling-based approaches.”
The YCB object and Model set: Towards common benchmarks for manipulation research (2015, methodology): “Inside OpenRAVE, the HERB robot uses CBiRRT, the OMPL [34] library and CHOMP to plan and optimize motion trajectories.”
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, methodology): “Inside OpenRAVE, the HERB robot uses CBiRRT, the OMPL [61] library and CHOMP to plan and optimize motion trajectories.”
Comparison of Trajectory Optimization Algorithms for High-Speed Quadrotor Flight Near Obstacles (2018, methodology): “A given set of waypoints is produced by first generating random start and goal locations, then using RRT* [9], in the Open Motion Planning Library [17] to plan a set of waypoints with collision-free paths between them.”
Motion Planning Networks (2018, methodology): “In these environments, we use a C++ OMPL [21] implementation of BIT* to compare against a C++ implementation of MPNet.”
Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments (2020, methodology): “For sampling-based algorithms we used OMPL’s implementations [16]”
PyRobot: An Open-source Robotics Framework for Research and Benchmarking (2019, background): “[20], OMPL [59] which provide hardware-agnostic core functionalities that can be compiled for each spe-ciﬁc robot.”
RVT: Robotic View Transformer for 3D Object Manipulation (2023, methodology): “To achieve the target gripper pose, we generate joint space actions by using the same sampling-based motion planner [63, 64] as in [5, 6].”
Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study (2014, background): “The Karvaki Lab’s Object-Oriented Programming System for Motion Planning (OOPSMP) [26], 2008, is a predecessor to the Open Motion Planning Library (OMPL) [27], 2010, both of which are collections of planning algorithms and components whose scope also excludes hardware execution and perception tasks.”
Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles (2021, methodology): “Three variants of RRT* implemented using OMPL [29] represent sampling-based planners, while CHOMP [5] is used as an optimization-based planner.2 The variants of the RRT* algorithm differ in how they randomly generate new planning states (here, positions in 3D space).”