Bidirectional Sampling-Based Motion Planning Without Two-Point Boundary Value Solution

Nayak, Sharan; Otte, Michael W.

doi:10.1109/tro.2022.3181947

Cited by 6 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…M can be customized by users; this paper suggests diag(0.32, 0.27, 0.23, 0.05, 0.2, and 0.05). The collision probability of node Q is estimated by Equation (7).…”

Section: Design Of Collision Classifiermentioning

confidence: 99%

“…Unlike a single rigid body such as a UAV or a mobile robot, the complexity of a manipulator prevents it from directly utilizing obstacle data in the workspace, which increases the complexity of map construction [5,6]. In response, a sampling-based motion planner (SBMP) has become an important solution for manipulator motion planning [7][8][9][10]. An SBMP gradually explores the required geometric paths by random sampling in configuration space (C-space) and by using a collision detection algorithm to ensure local path safety [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

View full text Add to dashboard Cite

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

show abstract

“…M can be customized by users; this paper suggests diag(0.32, 0.27, 0.23, 0.05, 0.2, and 0.05). The collision probability of node Q is estimated by Equation (7).…”

Section: Design Of Collision Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

Bi-HS-RRT$$^\text {X}$$: an efficient sampling-based motion planning algorithm for unknown dynamic environments

Liao,

Xu,

Zhou

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRT$$^\text {X}$$ X , which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.

show abstract

S*: On Safe and Time Efficient Robot Motion Planning

Laha

Sun

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Bidirectional Sampling-Based Motion Planning Without Two-Point Boundary Value Solution

Cited by 6 publications

References 39 publications

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Bi-HS-RRT$$^\text {X}$$: an efficient sampling-based motion planning algorithm for unknown dynamic environments

S*: On Safe and Time Efficient Robot Motion Planning

Contact Info

Product

Resources

About