scholarly journals Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohanan M. Gangadharan ◽  
Ambuja Salgaonkar
Keyword(s):  
Motion Planning ◽  
Ant Colony Optimization ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Robotic Motion

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 A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance

2018 ◽  
Vol 6 (3) ◽  
pp. 368-386 ◽  
Author(s):  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Huseyin Tunc ◽  
Linkan Bian ◽  
William Bullington
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Real Life ◽  
Dynamic Environments ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Ant Colony Optimization Algorithm ◽  
Pheromone Trails

Abstract This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. Highlights Proposed a novel ACO-ALNS based metaheuristic. Four variants of the proposed metaheuristic is developed to investigate the efficiency of each of them. A real life case study mirroring the behavior of DTSP is developed.

scholarly journals Exploratory path planning for mobile robots in dynamic environments with ant colony optimization

2020 ◽  
Author(s):  
Valéria de C. Santos ◽  
Fernando E. B. Otero ◽  
Colin Johnson ◽  
Fernando S. Osório ◽  
Cláudio F. M. Toledo
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Ant Colony Optimization ◽  
Dynamic Environments ◽  
Ant Colony

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.

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