An unsupervised adaptive strategy for constructing probabilistic roadmaps

Tapia, Lydia; Thomas, Shawna; Boyd, Bryan; Amato, Nancy M.

doi:10.1109/robot.2009.5152544

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…There are many ways in which to construct a set of regions [28][29][30][31]. Our region construction method is related to [30] but differs in two aspects.…”

Section: Region Constructionmentioning

confidence: 99%

“…Although there exist many sampling strategies, no one outperforms all others for all problem instances. To achieve a better distribution of samples, different sampling strategies are combined in [27, 28]. In addition, region-based methods [29,30] divide the configuration space into some local regions and use local region information to decide where to boost sampling intelligently, or which sampler to apply.…”

Section: Introductionmentioning

confidence: 99%

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A Region-Specific Hybrid Sampling Method for Optimal Path Planning

Zhong

Liu

2016

International Journal of Advanced Robotic Systems

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Finding high quality paths within a limited time in configuration space is a challenging issue for path planning. Recently, an asymptotically optimal method called fast marching tree (FMT*) has been proposed. This method converges significantly faster than its state-of-the-art counterparts when addressing a wide range of problems. However, FMT* appears unable to solve the narrow passage problem in optimal path planning, since it is based on uniform sampling. Aiming at solving this problem, a novel region-based sampling method integrating global scenario information and local region information is proposed in this paper. First, global information related to configuration space is extracted from an initial sample set obtained via hybrid sampling. Then, local regions are constructed and local region information is captured to make intelligent decisions regarding regions that are difficult and need to be boosted. Finally, the initial sample set is sent to FMT* using a modified locally optimal onestep connection strategy in order to find an initial and feasible solution. If no solution is found and time permits, the guided hybrid sampling will be adopted in order to add more useful samples to the sample set until a solution is found or the time for doing so runs out. Simulation results for six benchmark scenarios validate that our method can achieve significantly better results than other state-of-theart methods when applied in challenging scenarios with narrow passages.

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“…There are many ways in which to construct a set of regions [28][29][30][31]. Our region construction method is related to [30] but differs in two aspects.…”

Section: Region Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Region-Specific Hybrid Sampling Method for Optimal Path Planning

Zhong

Liu

2016

International Journal of Advanced Robotic Systems

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“…Unfortunately, this only applies to PRMs. Other methods use visibility to bias samples as well, e.g., feature-sensitive motion planning [18], [20], [28] which adapts the sampling method based upon a decomposition and classification of regions. Hybrid PRM [8] uses a cost-sensitive adaptive strategy to select sampling methods for PRMs.…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

“…For PRMs visibility is approximated as a simple ratio of successful connections over total connection attempts, calculated during the node connection phase. This has been used to filter sampling to structurally improve the roadmap [22], and exploited for heterogeneous environments in [18], [28]. However, it is nontrivial to utilize visibility like this in RRTs.…”

Section: Introductionmentioning

confidence: 99%

Adapting RRT growth for heterogeneous environments

Denny

Morales

Rodríguez

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. 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. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately.

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“…An important consequence of random sampling in the construction of Probabilistic roadmap (PRM) representations is that two roadmaps representing the same environment are usually topologically different and return homotopically different paths. The use of different sampling methods, local planners, and construction strategies [19], [27] introduces even more variability. The heterogeneity that naturally arises in constructing PRMs makes them an excellent focus for considering representational heterogeneity in this work.…”

Section: Related Work a Motion Planningmentioning

confidence: 99%

Multi-robot caravanning

Denny

Giese

Mahadevan

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We study multi-robot caravanning, which is loosely defined as the problem of a heterogeneous team of robots visiting specific areas of an environment (waypoints) as a group. After formally defining this problem, we propose a novel solution that requires minimal communication and scales with the number of waypoints and robots. Our approach restricts explicit communication and coordination to occur only when robots reach waypoints, and relies on implicit coordination when moving between a given pair of waypoints. At the heart of our algorithm is the use of leader election to efficiently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment.We implement our approach both in simulation and on a physical platform, and characterize the performance of the approach under various scenarios. We demonstrate that our approach can successfully be used to combine the planning capabilities of different agents.

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An unsupervised adaptive strategy for constructing probabilistic roadmaps

Cited by 9 publications

References 22 publications

A Region-Specific Hybrid Sampling Method for Optimal Path Planning

A Region-Specific Hybrid Sampling Method for Optimal Path Planning

Adapting RRT growth for heterogeneous environments

Multi-robot caravanning

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