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Electronics 2016, 5(2), 16; doi:10.3390/electronics5020016

Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps

Department of Information Technology, Indian Institute of Information Technology, Allahabad, Deoghat, Jhalwa, Allahabad 211012, India
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Academic Editor: Mostafa Bassiouni
Received: 26 December 2015 / Revised: 5 March 2016 / Accepted: 21 March 2016 / Published: 6 April 2016
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Abstract

Motion planning deals with finding a collision-free trajectory for a robot from the current position to the desired goal. For a high-dimensional space, sampling-based algorithms are widely used. Different sampling algorithms are used in different environments depending on the nature of the scenario and requirements of the problem. Here, we deal with the problems involving narrow corridors, i.e., in order to reach its destination the robot needs to pass through a region with a small free space. Common samplers used in the Probabilistic Roadmap are the uniform-based sampler, the obstacle-based sampler, maximum clearance-based sampler, and the Gaussian-based sampler. The individual samplers have their own advantages and disadvantages; therefore, in this paper, we propose to create a hybrid sampler that uses a combination of sampling techniques for motion planning. First, the contribution of each sampling technique is deterministically varied to create time efficient roadmaps. However, this approach has a limitation: The sampling strategy cannot adapt as per the changing configuration spaces. To overcome this limitation, the sampling strategy is extended by making the contribution of each sampler adaptive, i.e., the sampling ratios are determined on the basis of the nature of the environment. In this paper, we show that the resultant sampling strategy is better than commonly used sampling strategies in the Probabilistic Roadmap approach. View Full-Text
Keywords: motion planning; sampling-based motion planning; configuration space; probabilistic roadmap; robotics motion planning; sampling-based motion planning; configuration space; probabilistic roadmap; robotics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kannan, A.; Gupta, P.; Tiwari, R.; Prasad, S.; Khatri, A.; Kala, R. Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps. Electronics 2016, 5, 16.

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