EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles

Jaillet, L.; Hoffman, J.; Berg, Joost van den; Abbeel, P.; Porta, Josep M.; Goldberg, Ken

doi:10.1109/iros.2011.6048409

Cited by 10 publications

(9 citation statements)

References 12 publications

(23 reference statements)

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“…There are several extensions of the RRT algorithm to deal with uncertainty (Jaillet et al, 2011;Belghith et al, 2013;Bry and Roy, 2011;Achtelik et al, 2013) which mostly deal with dynamic obstacles and show poor performances when facing other forms of uncertainty such as imperfect sensing or noisy environment maps. In the field of multi-query algorithms, several extensions of the PRM planner have been introduced to deal with uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Khaksar

Uddin

Tørresen

2019

International Journal of Applied Mathematics and Computer Science

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Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.

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Section: Related Workmentioning

confidence: 99%

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Khaksar

Uddin

Tørresen

2019

International Journal of Applied Mathematics and Computer Science

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“…10 The method is proposed based on retraction-based RRT (RRRT), which tested to be efficient than dynamicdomain RRT (DDRRT). 21 The study by Cheng 22 solved narrow passage problem with uncertainty using environment-guided RRT. Environment-guided RRT integrates the merits of reachability-guided RRT, 23 which records the failures of exploration to increase the change in narrow passage, and resolution-complete RRT, 24 which can obtain system dynamics to avoid useless extension.…”

Section: Related Workmentioning

confidence: 99%

Gart

Yang

Xiao

et al. 2016

International Journal of Advanced Robotic Systems

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The problem of three-dimensional path planning in obstacle-crowded environments is a challenge (an NP-hard problem), which becomes even more complex when considering environmental uncertainty and system control. Int this paper, we mainly focused on more challenging problem, that is, path planning in obstacle-crowded environments, and we try to find the relation between contact information and obstacle modeling. We proposed a newactive exploring sampling-based algorithm based on rapidly exploring random tree (RRT), namely, guiding attraction-based random tree (GART). GART introduces bidirectional potential field to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. Furthermore, dynamic constraints are deployed to establish forward extending region by GART. Thus, GART can ensure kinodynamic reachability as well as smoothness. Theoretical analysis demonstrate that GART is probabilistic complete, and it obtains faster convergence rate because of its redistribution ability. In addition to theoretical analysis, this article provides comparative simulations as well as experiments under typical situations. Results demonstrate that GART has a much better time-efficiency performance than RRT*, retraction-based RRT, and other referred algorithms when applying redistribution and dynamic constraints on random exploration. KeywordsSampling-based planning algorithm, bidirectional potential field, control and sensing uncertainty, percentage of useful samples Date

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“…In [11], an MST is created with weighted C space cells, where weights depend on distance to boundary. In [12], an environment-guided variant of RRT is designed for kinodynamic robot systems that estimates the probability of collision under uncertainty in control and sensing. In [13], spherical volumes are used instead of points for nodes in RRT tree expansion, resulting in formation of a sparse tree.…”

Section: Introductionmentioning

confidence: 99%

A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners

Shukla¹,

Kumar²,

Bera³

et al. 2021

Preprint

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Sampling based probabilistic roadmap planners (PRM) have been successful in motion planning of robots with higher degrees of freedom, but may fail to capture the connectivity of the configuration space in scenarios with a critical narrow passage. In this paper, we show a novel technique based on Lévy Flights to generate key samples in the narrow regions of configuration space, which, when combined with a PRM, improves the completeness of the planner. The technique substantially improves sample quality at the expense of a minimal additional computation, when compared with pure random walk based methods, however, still outperforms state of the art random bridge building method, in terms of number of collision calls, computational overhead and sample quality. The method is robust to the changes in the parameters related to the structure of the narrow passage, thus giving an additional generality. A number of 2D & 3D motion planning simulations are presented which shows the effectiveness of the method.

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EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles

Cited by 10 publications

References 12 publications

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Gart

A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners

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