Sparsification of motion-planning roadmaps by edge contraction

Shaharabani, Doron; Salzman, Oren; Agarwal, Pankaj K.; Halperin, Dan

doi:10.1109/icra.2013.6631155

Cited by 12 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IRS has an equivalent success ratio to PRM*, but the success ratio of SPARS suffers in harder instances. provide compact alternatives with near-optimality guarantees [5], [15], [20], [24]. The evaluation of these methods presented here shows the benefits of investing computational power to compute roadmap data structures on the cloud, which are then used to answer motion-planning queries on the robot's local workstation given an updated model of its surroundings.…”

Section: Discussionmentioning

confidence: 99%

“…Recent research efforts have focused on this tradeoff and aim toward compact motion-planning representations with desirable near-optimality guarantees in terms of path quality [5], [15], [20], [24]. There is a naive alternative for creating a more compact roadmap by randomly removing edges and nodes up to a desired level of sparsity.…”

Section: Effects Of Roadmap Properties On Cloud-based Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Bekris

Shome

Krontiris

et al. 2015

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

T he goal of this article is to highlight the benefits of cloud automation for industrial adopters and some of the research challenges that must be addressed in this process. The focus is on the use of cloud computing for efficiently planning the motion of new robot manipulators designed for flexible manufacturing floors. In particular, different ways that a robot can interact with a computing cloud are considered, where an architecture that splits computation between the remote cloud and the robot appears advantageous. Given this synergistic robot-cloud architecture, this article describes how solutions from the recent literature can be employed on the cloud during a periodically updated preprocessing phase to efficiently answer manipulation queries on the robot given changes in the workspace. In this setup, interesting tradeoffs arise between path quality and computational efficiency, which are evaluated through simulation. These tradeoffs motivate further research on how motion planning should be executed given access to a computing cloud.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Effects Of Roadmap Properties On Cloud-based Schemesmentioning

confidence: 99%

Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Bekris

Shome

Krontiris

et al. 2015

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

show abstract

“…In this case, xnew is not compared in terms of its path cost with any existing tree node. The edge x selected → xnew is added to the tree and a witness at the location of xnew is added to the set of witnesses S. and others (Wang et al 2013, Shaharabani et al 2013. The discussion section of this paper describes the trade-offs that arise between computational efficiency and the type of guarantee achieved in relation to the requirement for the existence of δ-robust trajectories.…”

Section: Change In Algorithmic Paradigmmentioning

confidence: 99%

“…Anytime (Karaman et al, 2011) and lazy (Alterovitz et al, 2011) variants of RRT* have also been proposed. There are also techniques that provide asymptotic near-optimality using sparse roadmaps, which inspire the current work (Dobson and Bekris, 2014; Dobson et al, 2012; Marble and Bekris, 2011, 2013; Shaharabani et al, 2013; Wang et al, 2013). Sparse trees appear in the context of feedback-based motion planning (Tedrake, 2009).…”

Section: Introductionmentioning

confidence: 99%

Asymptotically optimal sampling-based kinodynamic planning

Littlefield

Bekris³

2016

The International Journal of Robotics Research

219

178

View full text Add to dashboard Cite

IJRR 000(00):1-40 ©The Author(s) 2010 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI:doi number AbstractSampling-based algorithms are viewed as practical solutions for high-dimensional motion planning. Recent progress has taken advantage of random geometric graph theory to show how asymptotic optimality can also be achieved with these methods. Achieving this desirable property for systems with dynamics requires solving a two-point boundary value problem (BVP) in the state space of the underlying dynamical system. It is difficult, however, if not impractical, to generate a BVP solver for a variety of important dynamical models of robots or physically simulated ones. Thus, an open challenge was whether it was even possible to achieve optimality guarantees when planning for systems without access to a BVP solver. This work resolves the above question and describes how to achieve asymptotic optimality for kinodynamic planning using incremental sampling-based planners by introducing a new rigorous framework. Two new methods, STABLE_SPARSE_RRT (SST) and SST * , result from this analysis, which are asymptotically near-optimal and optimal, respectively. The techniques are shown to converge fast to high-quality paths, while they maintain only a sparse set of samples, which makes them computationally efficient. The good performance of the planners is confirmed by experimental results using dynamical systems benchmarks, as well as physically simulated robots.

show abstract

“…Most of the references mentioned do not consider storage requirements for their roadmaps. (Shaharabani et al 2013) (Dobson and Bekris 2014) propose algorithms to reduce the size of path planning roadmaps. However, these algorithms applied in the context of surgical training will result in loss of coverage, connectivity and quality.…”

Section: Related Workmentioning

confidence: 99%

Safety-Assured Offline-Online Path Planning with Reduced Storage Requirements in Simulated Surgical Training

2017

Modeling and Simulation in Medicine Symposium (MSM 2017)

View full text Add to dashboard Cite

Optimal path planning and re-planning are essential requirements for efficient robotic and laparoscopy surgical education as they guide instruments through the operating field. Ensuring collision free paths and training under uncertainty, with limited dependence on an expert surgeon's assistance, are key aspects to be considered. In this paper, a hybrid path planning method is introduced to assist in surgical training that combines offline and online approaches for trajectory generation. The proposed offline-online path planner integrates features of offline approaches that guarantee safe navigation with real-time re-planning capability of online planners. It reduces memory storage requirements of the trajectories for online path planning, by more effectively saving them as segments, catering to the portability of the surgical systems. The efficacy of the proposed approach is demonstrated in a simulated training environment for hand-eye coordination tasks on the Computer Assisted Surgical Trainer (CAST) platform.

show abstract

Sparsification of motion-planning roadmaps by edge contraction

Cited by 12 publications

References 31 publications

Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Asymptotically optimal sampling-based kinodynamic planning

Safety-Assured Offline-Online Path Planning with Reduced Storage Requirements in Simulated Surgical Training

Contact Info

Product

Resources

About