Mobile robot motion planning by point to point based on modified ant colony optimization and Voronoi diagram

2016 ◽  
Author(s):  
Nukman Habib ◽  
Djoko Purwanto ◽  
Adi Soeprijanto
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Voronoi Diagram ◽  
Ant Colony ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Point To Point

Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

2015 ◽  
Vol 776 ◽  
pp. 396-402 ◽  
Author(s):  
Nukman Habib ◽  
Adi Soeprijanto ◽  
Djoko Purwanto ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Initial Position ◽  
Ant Colony ◽  
Robot Motion ◽  
Complex Environments ◽  
Point To Point ◽  
Global Planning

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

Two-level artificial intelligent system of mobile robot motion planning

2014 ◽  
Vol 10 ◽  
pp. 50-54
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Control Systems ◽  
Intelligent System ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Vision Systems ◽  
Artificial Intelligent ◽  
Upper Level

We consider a two-level intelligent system for planning the movements of mobile robots, in which the search for the trajectory is carried out on two levels — a rough and precise planning subsystems. Insufficient resolution of vision systems at the upper level is compensated by sensor systems placed on board robots. The proposed approach reduces the resources required on-board control systems (are based on computer or controller) and optimization of traffic routes of all members of the group to achieve group goals.

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