SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

Abstract

Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO) and Genetic Algorithms (GA) for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO), GA algorithms. It also outperforms the Dijkstra exact method in solving the path planning problem for large graph environments. It improves the solution quality up to 57% in comparison with CACO and reduces the execution time up to 83% as compared to Dijkstra for large and dense graphs. In addition, the experimental results on a real robot shows that smartPATH finds the optimal path with a probability up to 80% with a small gap not exceeding 1m in 98%.

An Improved Ant Colony System Algorithm for Robot Path Planning and Performance Analysis

“…Mobile robot path planning is a fundamental study in the field of mobile robots. Path planning is where the robot can navigate safely and find a motion path with minimum energy consuming from one point to the other [2].…”

“…At the same time, these methods are inefficient, have poor global search abilities, and are insufficiently adaptable to complex domains. Improved ant colony optimization has drawn increasing attention from researchers and has been used in a variety of applications [2]- [27], including mobile robot path planning problems [6]- [13]. The algorithm has several advantages including strong robustness, excellent distributed computing, easy integration with other algorithms, and strong global optimization performance.…”

Mobile robot path planning based on social interaction space in social environment

“…Path planning is a key technique of mobile robot navigation 1 and a hot issue in mobile robot research. 2 The ability to safely navigate in crowded and dynamic environments becomes crucial for mobile robots employed in indoor environments, such as shopping malls, museums, or schools.…”

Modeling and Processing of Cloud Computing and It’s Performance Measurement

“…Inspired from this behavior of real ants, Marco Dorigo proposed the ACO metaheuristic [5] in the early 1990s to find approximate solutions to difficult optimization problems. ACO was initially applied to the travelling salesman problem (TSP), and then it has been used to solve several research problems such as the path planning problem for a mobile robot [6].…”