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Robot Motion Planning in an Unknown Environment with Danger Space

1
Department of Aerospace Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14395-1561, Iran
2
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
3
Nonlinear Systems and Applications, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
4
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5
National Council for Science and Technology Policy, Hanoi, Vietnam
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(2), 201; https://doi.org/10.3390/electronics8020201
Received: 17 January 2019 / Revised: 4 February 2019 / Accepted: 7 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Motion Planning and Control for Robotics)
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Abstract

This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods. View Full-Text
Keywords: robot path planning; danger space; unknown environment; modified Markov decision processes robot path planning; danger space; unknown environment; modified Markov decision processes
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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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Jahanshahi, H.; Jafarzadeh, M.; Sari, N.N.; Pham, V.-T.; Huynh, V.V.; Nguyen, X.Q. Robot Motion Planning in an Unknown Environment with Danger Space. Electronics 2019, 8, 201.

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