Fuzzy Counter Ant Algorithm for Maze Problem

Abstract

UTONOMOUS intelligent multi-agents have a widespread application in different fields including homeland security. Several applications of such autonomous robots include exploration, mine detection, border patrol, ISR missions etc. It is desired that the autonomous robot should have some adaptive and intelligent decision making capability for navigation in unknown obstacle rich terrains. Researchers in the field of AI and neural networks have considered the

Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

“…Fig. 14 While the benchmark outperforms our approaches (iAA and iAA-B) for a small group sizes, our technique iAA beats the benchmark Ahuja (2010) as the swarm size increases. Furthermore, an extended study shows that the trend of improvement in iAA is even more favorable as the swarm size continues to increase.…”

“…Algorithm Used (Wang et al, 2017) Improved ACO (Malone et al, 2017) Artificial Potential Field Method (Atten et al, 2016) Multi Pheromones for tracking targets (Cao, 2016) Improved ACO Heuristic Function (Krentz et al, 2015) Simple ACO (Fossum et al, 2014) Repellent Pheromone for coverage (Deepak et al, 2014) Advanced PSO (Wang and Wang, 2013) Genetic Algorithm with ACO (Aurangzeb et al, 2013) Hybrid ACO w. Random-+ RL based-Search (Buniyamin et al, 2011) Point Bug Algorithm (Wang et al, 2011) Dijkstra Algorithm (Ahuja, 2010) Fuzzy Logic with Counter ACO (Gong et al, 2009) PSO in partially known environments (Sauter et al, 2005) Combination of multiple pheromones Deterministic techniques have been successfully applied to path planning (Mac et al, 2016;Yang, 2009). Some prominent techniques include searches on Visibility Graphs (VG) and Voronoi diagrams (VD) (Leena and Saju, 2014), Cell Decomposition Method (Mac et al, 2016), and gradient techniques like the Artificial Potential Field Method (Bounini et al, 2017;Khatib, 1986;Sutantyo et al, 2010), amongst others.…”

“…As a benchmark, we compared the performance of our solution with another study that used ACO coupled with Fuzzy Logic and also presented results in terms of number of moves needed to solve the maze Ahuja (2010). For the purpose of benchmark comparison, we set up mazes to the other study with the same sizes of 8 × 8 and 15 × 15 units.…”

Fuzzy counter-ant for avoiding the stagnation of multirobot exploration

“…Let' s consider another view of swarm intelligence inspired from ant colony optimization called Counter-Ant Algorithm (CAA) [17][18][19] . In reality, the robots' collaborative behavior is based on repulsion instead of attraction to pheromone-a chemical evaporating matter-that represents the core of ants' cooperation.…”

“…In recent years, research has been oriented more towards the development of real-time systems and particularly the study of hybrid methods to ensure scalability of behavior with respect to the dynamic nature of the environment [16] . In [17] , the authors present a hybrid path planning method, based on a counter ant algorithm and a fuzzy inference system, which enables multiple agents to find a solution rapidly along the unexplored regions. To improve the uses of the ant algorithm, we present in this paper a solution based on fuzzy reasoning for the problem of stagnation behavior of evolving ant robots.…”