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Workspace-Based Connectivity Oracle: An Adaptive Sampling Strategy for PRM Planning
[chapter]
Springer Tracts in Advanced Robotics
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This paper presents Workspace-based Connectivity Oracle (WCO), a dynamic sampling strategy for probabilistic roadmap planning. ...
These component samplers are combined through the adaptive hybrid sampling approach, based on their sampling histories. ...
Core to our new planner is a new sampling strategy called Workspace-based Connectivity Oracle (WCO). WCO is an ensemble sampler composed of many component samplers. ...
doi:10.1007/978-3-540-68405-3_3
fatcat:iwbpfqjwonhnblgx2s3nkwewqu
Adapting RRT growth for heterogeneous environments
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. ...
Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. ...
Hybrid PRM [8] uses a cost-sensitive adaptive strategy to select sampling methods for PRMs. ...
doi:10.1109/iros.2013.6696589
dblp:conf/iros/DennyMRA13
fatcat:mq7lnbskdvge5p4dpnambvbdom
Integrating multiple soft constraints for planning practical paths
2014
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
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This approach uses an adaptive bidding strategy for each optimizer and in each round the optimizer with the best predicted performance is selected. ...
Sampling-based algorithms are a common approach to high-dimensional real-world path planning problems. ...
The core of sampling-based path planners involves searching for a path by sampling and testing motions connecting the samples. ...
doi:10.1109/iros.2014.6942977
dblp:conf/iros/YangDJ14
fatcat:pzdmhwzharfylojfb3trqo6tbu
A scalable method for parallelizing sampling-based motion planning algorithms
2012
2012 IEEE International Conference on Robotics and Automation
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This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. ...
We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. ...
Acknowledgement The authors would like to thank Dezshaun Meeks for his contributions as an undergraduate research intern in our lab during the summer of 2011. ...
doi:10.1109/icra.2012.6225334
dblp:conf/icra/JacobsMBDTA12
fatcat:ghmp6mqygzdmrejf3pfe5fkluu
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
[article]
2020
arXiv
pre-print
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This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. ...
sample-based planners in a hybrid approach while still retaining significant computational and optimality improvements. ...
[35] proposed to adapt the sampling for the SMPs using REINFORCE algorithm [36] in discretized workspaces. Berenson et al. ...
arXiv:1907.06013v3
fatcat:a7ynhrxnobcoloo6faf4np5rq4
Faster Sample-Based Motion Planning Using Instance-Based Learning
[chapter]
2013
Springer Tracts in Advanced Robotics
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Our approach is general, makes no assumption about the sampling scheme, and can be used with various sample-based motion planners, including PRM, Lazy-PRM, RRT and RRT * , by making small changes to these ...
We present a novel approach to improve the performance of sample-based motion planners by learning from prior instances. ...
[11] adaptively combine multiple sampling strategies to improve the roadmap's connectivity. ...
doi:10.1007/978-3-642-36279-8_23
fatcat:a37amgfnivagleoas7mnl5pl7u
Multilevel Motion Planning: A Fiber Bundle Formulation
[article]
2020
arXiv
pre-print
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Given such a structure and a corresponding admissible constraint function, we can develop highly efficient and optimal search-based motion planning methods for high-dimensional state spaces. ...
Third, we develop a novel recursive path section method based on an L1 interpolation over path restrictions, which we use to quickly find feasible path sections. ...
Acknowledgement The authors disclose receipt of the following financial support for the research, authorship and publication of this article: ...
arXiv:2007.09435v1
fatcat:oaxpk5576fcdjfrirmhanlvdgm
dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning
2019
Autonomous Robots
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2019 pre-print arXiv:1903.00994v1 fatcat:wbft4dc2orhdfap4wdcp3i4nuq
In particular, the proposed \drrtstar\ is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, \drrt. ...
Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. ...
n samples for each PRM. ...
doi:10.1007/s10514-019-09832-9
fatcat:exjujmlb5jclnordtnm5m5lb2i
Learning Implicit Sampling Distributions for Motion Planning
[article]
2018
arXiv
pre-print
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In this paper, a policy- search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. ...
Our method can be incor- porated with a variety of sampling-based planners to improve performance. ...
The contribution of this paper is to 1) present an adaptive approach to generating good probability distributions for different environments that improve the performance of sampling based planners and ...
arXiv:1806.01968v1
fatcat:my6zcxksv5f7jm34aptbeuag4i
Blind RRT: A probabilistically complete distributed RRT
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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Within sampling-based motion planning, we find two main approaches which most strategies follow: graph-based approaches, e.g., Probabilistic Roadmaps (PRM) [6] , and tree-based approaches, e.g., Rapidly-Exploring ...
sampling-based motion planning. ...
doi:10.1109/iros.2013.6696587
dblp:conf/iros/RodriguezDJTA13
fatcat:sy6bcfqykfaldbu6efrodigi2e
Constrained Motion Planning Networks X
[article]
2021
arXiv
pre-print
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Motion Planning methods for quickly solving complex constrained planning tasks. ...
We also introduce neural task and scene representations conditioned on which the CoMPNetX generates implicit manifold configurations to turbo-charge any underlying classical planner such as Sampling-based ...
ACKNOWLEDGMENTS We thank Dmitry Berenson and Frank Park for their insightful discussions and sharing their algorithms' implementations. ...
arXiv:2010.08707v2
fatcat:rd2drgc5jvgyflwuf24mazzafy
Fast probabilistic collision checking for sampling-based motion planning using locality-sensitive hashing
2016
The international journal of robotics research
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We present a novel approach to perform fast probabilistic collision checking in high-dimensional configuration spaces to accelerate the performance of sampling-based motion planning. ...
We evaluate the benefit of our probabilistic collision checking approach by integrating it with a wide variety of sampling-based motion planners, including PRM, lazyPRM, RRT, and RRT * . ...
However, our approach is independent of the underlying sampling strategy, and thus can be combined with all the adaptive sampling strategies mentioned above for better performance. ...
doi:10.1177/0278364916640908
fatcat:ltztd6csirhkxdropspw3w2rfa
NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments
[article]
2022
arXiv
pre-print
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In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). ...
Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. ...
An alternative for planning under uncertainty is the chance-constraint strategy [18] - [20] . ...
arXiv:2205.06951v1
fatcat:bq6kwbip3vaiffo2wgnrqzh3zq
Learning value functions with relational state representations for guiding task-and-motion planning
2019
Conference on Robot Learning
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We propose a novel relational state representation and an action-value function learning algorithm that learns from planning experience for geometric task-and-motion planning (GTAMP) problems, in which ...
It supports efficient learning, using graph neural networks, of an action-value function that can be used to guide a GTAMP solver. ...
Algorithm 1 defines sampling-based abstract-edge heuristic search (SAHS), which is a greedy strategy with respect to the priority function. ...
dblp:conf/corl/KimS19
fatcat:hhsfjcrnpvajdbl6pwdj6enxlm
Free-configuration biased sampling for motion planning
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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In sampling-based motion planning algorithms the initial step at every iteration is to generate a new sample from the obstacle-free portion of the configuration space. ...
This strategy is rarely questioned because the extra work associated with sampling (and then rejecting) useless points contributes at most a constant factor to the planning algorithm's asymptotic runtime ...
Related work in adaptive sampling for sampling-based motion planning follows. Hsu et al. ...
doi:10.1109/iros.2013.6696513
dblp:conf/iros/BialkowskiOF13
fatcat:szvhbbfpg5bz7i2bqaaggkhtg4
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