Path planning of 6-DOF humanoid manipulator based on improved ant colony algorithm

Fen, LI; Zhao, Jingjin; Song, Xianfeng; Pei-ying, Zhou; Fang, Shihui; Zhong-jie, Liu

doi:10.1109/ccdc.2012.6244666

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Shimizu and Sugihara (2012) proposed a path planning approach for humanoids based on transitional sequence of the double support phase. Fen et al (2012) improved basic ant colony based optimization (ACO) technique for path planning of a humanoid manipulator. Kanoun et al (2011) developed a path planning approach for humanoids based on foot placements.…”

Section: Introductionmentioning

confidence: 99%

Archives of Control Sciences

Kumar¹,

Sahu²,

Parhi³

2018

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In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids. The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment. The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments. The results obtained from both the environments are compared against each other, and a good agreement between them is observed. Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.

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Section: Introductionmentioning

confidence: 99%

Archives of Control Sciences

Kumar¹,

Sahu²,

Parhi³

2018

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Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Kumar

Parhi

2022

Robotica

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Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

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6R Serial Manipulator Space Path Planning Based on RRT

Zhang²,

Zhao³

et al. 2016

2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

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Path planning of 6-DOF humanoid manipulator based on improved ant colony algorithm

Cited by 3 publications

References 8 publications

Archives of Control Sciences

Archives of Control Sciences

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

6R Serial Manipulator Space Path Planning Based on RRT

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