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Workspace-Based Connectivity Oracle: An Adaptive Sampling Strategy for PRM Planning [chapter]

Hanna Kurniawati, David Hsu
Springer Tracts in Advanced Robotics  
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

Jory Denny, Marco Morales, Samuel Rodriguez, Nancy M. Amato
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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

Jing Yang, Patrick Dymond, Michael Jenkin
2014 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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

Sam Ade Jacobs, Kasra Manavi, Juan Burgos, Jory Denny, Shawna Thomas, Nancy M. Amato
2012 2012 IEEE International Conference on Robotics and Automation  
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]

Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. Yip
2020 arXiv   pre-print
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]

Jia Pan, Sachin Chitta, Dinesh Manocha
2013 Springer Tracts in Advanced Robotics  
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]

Andreas Orthey and Sohaib Akbar and Marc Toussaint
2020 arXiv   pre-print
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

Rahul Shome, Kiril Solovey, Andrew Dobson, Dan Halperin, Kostas E. Bekris
2019 Autonomous Robots  
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]

Clark Zhang, Jinwook Huh, Daniel D. Lee
2018 arXiv   pre-print
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

Cesar Rodriguez, Jory Denny, Sam Ade Jacobs, Shawna Thomas, Nancy M. Amato
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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]

Ahmed H. Qureshi, Jiangeng Dong, Asfiya Baig, Michael C. Yip
2021 arXiv   pre-print
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

Jia Pan, Dinesh Manocha
2016 The international journal of robotics research  
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]

Fei Meng, Liangliang Chen, Han Ma, Jiankun Wang, Max Q.-H. Meng
2022 arXiv   pre-print
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

Beomjoon Kim, Luke Shimanuki
2019 Conference on Robot Learning  
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

Joshua Bialkowski, Michael Otte, Emilio Frazzoli
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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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