• Corpus ID: 240353769

Relevant Region Sampling Strategy with Adaptive Heuristic Estimation for Asymptotically Optimal Motion Planning

@article{Li2021RelevantRS,
  title={Relevant Region Sampling Strategy with Adaptive Heuristic Estimation for Asymptotically Optimal Motion Planning},
  author={Chenming Li and Fei Meng and Han Ma and Jiankun Wang and Max Q.‐H. Meng},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.00383}
}
The sampling-based motion planning algorithms can solve the motion planning problem in high-dimensional state space efficiently. This article presents a novel approach to sample in the promising region and reduce planning time remarkably. The RRT# defines the Relevant Region according to the cost-tocome provided by the optimal forward-searching tree; however, it takes the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. We propose a batch… 

Figures from this paper

References

SHOWING 1-10 OF 31 REFERENCES

Use of relaxation methods in sampling-based algorithms for optimal motion planning

  • O. ArslanP. Tsiotras
  • Computer Science
    2013 IEEE International Conference on Robotics and Automation
  • 2013
TLDR
This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG), denoted by RRT# (RRT “sharp”), which also guarantees asymptotic optimality, but, in addition, it also ensures that the constructed spanning tree rooted at the initial state contains lowest-cost path information for vertices which have the potential to be part of the optimal solution.

Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning

  • S. JoshiP. Tsiotras
  • Computer Science
    2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2020
TLDR
This work proposes an algorithm to sample the "Relevant Region", which is a subset of the Informed Set, which utilizes cost-to-come information from the planner’s tree structure, reduces dependence on the heuristic, and further focuses the search.

Sampling-based algorithms for optimal motion planning

TLDR
The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.

Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions

TLDR
This paper proves asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius.

Application of Sampling-Based Motion Planning Algorithms in Autonomous Vehicle Navigation

TLDR
In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling- based algorithms.

RRT∗-Connect: Faster, asymptotically optimal motion planning

TLDR
An efficient asymptotically-optimal randomized motion planning algorithm solving single-query path planning problems using a bidirectional search that will contribute to increase the performance of autonomous robots and vehicles due to the reduced motion planning time in complex environments.

Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics

  • Marlin P. StrubJ. Gammell
  • Computer Science, Business
    2020 IEEE International Conference on Robotics and Automation (ICRA)
  • 2020
TLDR
Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*.

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

TLDR
Batch Informed Trees (BIT) is developed, shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

EB-RRT: Optimal Motion Planning for Mobile Robots

TLDR
An elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory.