A General Region-Based Framework for Collaborative Planning

  title={A General Region-Based Framework for Collaborative Planning},
  author={Jory Denny and Read Sandstr{\"o}m and Nancy M. Amato},
Sampling-based planning is a common method for solving motion planning problems. [] Key Method We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.

On the theory of user-guided planning

This paper classify and create simplistic models of common user-guided approaches, and the concept of expansiveness is extended to analyze these models to understand both when and how much user-guidance aids sampling-based planners.

Computation of Approximate Solutions for Guided Sampling-Based Motion Planning of 3D Objects

A modification of the iterative guiding process to avoid a situation where the part of the guiding path is too close to obstacles of the configuration space, which requires to estimate the surface of the obstacle region, which is achieved by detecting its boundary configurations during the sampling process.

Increasing Diversity of Solutions in Sampling-based Path Planning

This paper proposes an extension of the RRT algorithm to find diverse paths in the configuration space iteratively and shows that the proposed method finds more diverse trajectories than can be achieved by repeated computations of a single sampling-based planner.

Sampling-based motion planning of 3D solid objects guided by multiple approximate solutions

This work proposes to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot, and introduces the concept of disabled regions that are prohibited from the exploration using the sampling process.

Path planning of 3D solid objects using approximate solutions

A novel technique to reduce the geometry of the object by a combination of triangulation and iterative removal of surface triangles is proposed, suitable for both convex and non-convex objects.

Searching Multiple Approximate Solutions in Configuration Space to Guide Sampling-Based Motion Planning

A method to find multiple approximate solutions in the configuration space to increase the chance of finding the final solution of the narrow passage problem and the benefits are demonstrated by comparing the results with the state-of-the-art planners.

Motion planning of 3D objects using Rapidly Exploring Random Tree guided by approximate solutions

  • Vojtěch Vonásek
  • Computer Science
    2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)
  • 2018
A novel extension to Rapidly Exploring Random Tree (RRT) to cope with the narrow passage problem is proposed, which solves a simplified (relaxed) version of the problem which is achieved, e.g., by reducing the geometry of the robot.

User-Guided Path Planning for Redundant Manipulators in Highly Constrained Work Environments

This work presents a bi-directional tree-search framework for point-to-point path planning for manipulators that integrates human assistance seamlessly and computes high quality solutions in a variety of complex scenarios with a low failure rate.

Non-Prehensile Manipulation in Clutter with Human-In-The-Loop

It is shown that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates than the fully autonomous sampling-based planners.

Online Replanning With Human-in-the-Loop for Non-Prehensile Manipulation in Clutter — A Trajectory Optimization Based Approach

This work proposes an online-replanning method with a human-in-the-loop that enables a robot to plan and execute a trajectory autonomously, but also to seek high-level suggestions from a human operator if required at any point during execution.



A Region-Based Strategy for Collaborative Roadmap Construction

It is demonstrated that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.

Sampling-based roadmap of trees for parallel motion planning

The planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both and is significantly more decoupled than PRM and sampling-based tree planners.

Using Workspace Information as a Guide to Non-uniform Sampling in Probabilistic Roadmap Planners

  • J. V. D. BergM. Overmars
  • Computer Science
    IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
  • 2004
A hybrid technique is presented that combines the strengths of both methods, based on a robot independent cell decomposition of the free workspace guiding the probabilistic sampling more toward the interesting regions in the configuration space.

An obstacle-based rapidly-exploring random tree

A variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively and shows that both workspace obstacle information and C-space information can be used when deciding which direction to grow.

A Machine Learning Approach for Feature-Sensitive Motion Planning

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The Gaussian sampling strategy for probabilistic roadmap planners

This paper presents a new, simple sampling strategy, which it is called the Gaussian sampler, that gives a much better coverage of the difficult parts of the free configuration space.

An efficient retraction-based RRT planner

This work presents a novel optimization-based retraction algorithm to improve the performance of sample-based planners in narrow passages for 3D rigid robots and shows that the tree can grow closely towards any randomly generated sample.

Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning

Simulation experiments with dynamical models of ground and flying vehicles demonstrate that the combination of discrete search and motion planning in SyCLoP offers significant advantages, with speedups of up to two orders of magnitude.

Distributed Sampling-Based Roadmap of Trees for Large-Scale Motion Planning

  • E. PlakuL. Kavraki
  • Computer Science
    Proceedings of the 2005 IEEE International Conference on Robotics and Automation
  • 2005
This paper shows how to effectively distribute the computation of the Sampling-based Roadmap of Trees (SRT) algorithm using a decentralized master-client scheme and indicates that similar speedups can be obtained with several hundred processors.

RRT-connect: An efficient approach to single-query path planning

  • J. KuffnerS. LaValle
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two