Show simple item record

dc.contributor.authorJahanshahi, Hadi
dc.contributor.authorJafarzadeh, Mohsen
dc.contributor.authorSari, Naeimeh Najafizadeh
dc.contributor.authorViet-Thanh Pham
dc.contributor.authorVan Van Huynh
dc.contributor.authorXuan Quynh Nguyen
dc.date.accessioned2020-09-30T16:44:11Z
dc.date.available2020-09-30T16:44:11Z
dc.date.issued2019-02-10
dc.identifier.issn2079-9292
dc.identifier.urihttps://dx.doi.org/10.3390/electronics8020201
dc.identifier.urihttps://hdl.handle.net/10735.1/8959
dc.description.abstractThis 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.
dc.language.isoen
dc.publisherMDPI
dc.rightsCC BY 4.0 (Attribution)
dc.rights©2019 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRobots—Control systems
dc.subjectMarkov processes
dc.subjectAlgorithms (A-asterisk)
dc.subjectNavigation
dc.titleRobot Motion Planning in an Unknown Environment with Danger Space
dc.type.genrearticle
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationJahanshahi, Hadi, Mohsen Jafarzadeh, Naeimeh Najafizadeh Sari, , et al. 2019. "Robot Motion Planning in an Unknown Environment with Danger Space." Electronics 8(2): art. 201, doi: 10.3390/electronics8020201
dc.source.journalElectronics
dc.identifier.volume8
dc.identifier.issue2
dc.contributor.utdAuthorJafarzadeh, Mohsen
dc.contributor.ORCID0000-0001-7719-7081 (Jafarzadeh, M)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY 4.0 (Attribution)
Except where otherwise noted, this item's license is described as CC BY 4.0 (Attribution)