scholarly journals A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 280
Author(s):  
Viswanathan Sangeetha ◽  
Raghunathan Krishankumar ◽  
Kattur Soundarapandian Ravichandran ◽  
Fausto Cavallaro ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Performance Metrics ◽  
Optimal Path ◽  
Unmanned Vehicles ◽  
Ant Colony ◽  
Computational Overhead ◽  
Dynamic Scenario ◽  
Occupancy Grids ◽  
Feasible Path

Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.

scholarly journals Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 62107-62115 ◽  
Author(s):  
Qi Song ◽  
Qinglei Zhao ◽  
Shuxin Wang ◽  
Qiang Liu ◽  
Xiaohe Chen
Keyword(s):  
Fuzzy Logic ◽  
Path Planning ◽  
Ant Colony Optimization ◽  
Unmanned Vehicles ◽  
Ant Colony ◽  
Dynamic Path Planning ◽  
Dynamic Path

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.

scholarly journals Path planning for unmanned wheeled robot based on improved ant colony optimization

2020 ◽  
Vol 53 (5-6) ◽  
pp. 1014-1021 ◽  
Author(s):  
Lin Wang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Euclidean Distance ◽  
Optimal Path ◽  
Ant Colony ◽  
Main Concern ◽  
Random Maps ◽  
Wheeled Robot ◽  
Search Speed ◽  
Adaptive Heuristic

This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.

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