Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments

Liu, Hong; Sun, Qindong; Zhang, Tianwei

doi:10.1109/iros.2012.6385836

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…Furthermore, motion planning for humanoid robots with sampling-based algorithms has been explored; Kuffner et al presented algorithms for motion planning on humanoid robots with both the use of PRMs [30] and RRTs [31]. Other studies, such as the work of Liu et al [32], use RRTs for solving the stepping problem for humanoid robots.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Xanthidis¹,

Esposito²,

Rekleitis³

et al. 2018

Preprint

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Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a challenge. Applications include hyper-redundant manipulators, snake-like and humanoid robots. Based on the intuition that many of these problem instances do not require the robots to exercise every degree of freedom independently, we introduce an enhancement to popular sampling-based planning algorithms aimed at circumventing the exponential dependence on dimensionality. We propose beginning the search in a lower dimensional subspace of the configuration space in the hopes that a simple solution will be found quickly. After a certain number of samples are generated, if no solution is found, we increase the dimension of the search subspace by one and continue sampling in the higher dimensional subspace. In the worst case, the search subspace expands to include the full configuration space -making the completeness properties identical to the underlying sampling-based planer. Our experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and BidirectionalT-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot indicate that a solution is typically found much faster using this approach and the run time appears to be less sensitive to the dimension of the full configuration space. We explore important implementation issues in the sampling process and discuss its limitations.

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Section: Related Workmentioning

confidence: 99%

Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Xanthidis¹,

Esposito²,

Rekleitis³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Other attempts to combine sampling-based algorithms (such as RRT [29]) and finite action sets have been proposed [48,43,33]. In [45] and [42], a geometric property of the flea motion planning problem is generalized and used to transform footstep planning into a continuous problem, thus avoiding finite transition sets and enabling a direct application of traditional sampling-based algorithms.…”

Section: D Footstep Planning Problem Solution Via Finite Transition Setsmentioning

confidence: 99%

Biped Footstep Planning

Perrin¹

2017

Humanoid Robotics: A Reference

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Planning footsteps before the actual walking motion is a way to ignore the dynamic complexity of bipedal walking in the motion planning process without losing the possibility to exploit the essential characteristic of walking: the fact that it relies on isolated contacts with the ground, and therefore provides the ability to traverse irregular or discontinuous terrain. The duality between a continuous environment and discrete sequences of contacts confers to footstep planning a hybrid nature and an interesting theoretical aspect, which adds up to its obvious practical interest. Well incorporated and efficient footstep planning abilities would give humanoid robots a navigation autonomy allowing them to perform a great number of versatile tasks in unstructured environments. Many approaches have been considered to obtain efficient footstep planning, for example using only a finite number of predefined steps, or bounding boxes for fast collision checking. And for really reactive footstep planning, dynamics have to be put back into the equation, either by merging footstep planning with gait control, or by adaptively switching between different layers of planning. In this chapter, we give an overview of the main techniques that have been proposed and tested over the past 15 years.

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“…There is also some work on humanoid robots with sampling-based algorithms; Kuffner et al presented algorithms for motion planning on humanoid robots with both the use of PRMs [26] and RRTs [27]. Other studies, such as the work of Liu et al [28], which used RTTs for solving the stepping problem for humanoid robots.…”

Section: Related Workmentioning

confidence: 99%

RRT+ : Fast Planning for High-Dimensional Configuration Spaces

Xanthidis,

Rekleitis,

O'Kane

2016

Preprint

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In this paper we propose a new family of RRT based algorithms, named RRT + , that are able to find faster solutions in high-dimensional configuration spaces compared to other existing RRT variants by finding paths in lower dimensional subspaces of the configuration space. The method can be easily applied to complex hyperredundant systems and can be adapted by other RRT based planners. We introduce RRT + and develop some variants, called PrioritizedRRT + , PrioritizedRRT + -Connect, and PrioritizedBidirectionalT-RRT + , that use the new sampling technique and we show that our method provides faster results than the corresponding original algorithms. Experiments using the state-of-the-art planners available in OMPL show superior performance of RRT + for high-dimensional motion planning problems.

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Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments

Cited by 13 publications

References 15 publications

Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Biped Footstep Planning

RRT+ : Fast Planning for High-Dimensional Configuration Spaces

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