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.

scholarly journals Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

2020 ◽  
Vol 17 (3) ◽  
pp. 165-173
Author(s):  
C.O. Yinka-Banjo ◽  
U. Agwogie
Keyword(s):  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Ant Colony ◽  
Computational Time ◽  
Robot Path Planning ◽  
Nature Inspired Algorithms ◽  
Robot Path

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

scholarly journals Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1880 ◽  
Author(s):  
Fatin Hassan Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmad Taher Azar ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Superior Performance ◽  
Planning Problem ◽  
Planning Algorithms ◽  
Grid Based

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

Kinematics Analysis and Motion Control of a Mobile Robot Driven by Three Tracked Vehicles

2018 ◽  
Author(s):  
Xin-Jun Liu ◽  
Zhao Gong ◽  
Fugui Xie ◽  
Shuzhan Shentu
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Degrees Of Freedom ◽  
Inverse Kinematic ◽  
Planning Method ◽  
Tracked Vehicles ◽  
Loop Control ◽  
Point Motion ◽  
Point To Point ◽  
Motion Mode

In this paper, a mobile robot named VicRoB with 6 degrees of freedom (DOFs) driven by three tracked vehicles is designed and analyzed. The robot employs a 3-PPSR parallel configuration. The scheme of the mechanism and the inverse kinematic solution are given. A path planning method of a single tracked vehicle and a coordinated motion planning of three tracked vehicles are proposed. The mechanical structure and the electrical architecture of VicRoB prototype are illustrated. VicRoB can achieve the point-to-point motion mode and the continuous motion mode with employing the motion planning method. The orientation precision of VicRoB is measured in a series of motion experiments, which verifies the feasibility of the motion planning method. This work provides a kinematic basis for the orientation closed loop control of VicRoB whether it works on flat or rough road.

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