smartPATH: A hybrid ACO-GA algorithm for robot path planning

Abstract

Path planning is a critical combinatorial problem essential for the navigation of a mobile robot. Several re search initiatives, aiming at providing optimized solutions to this problem, have emerged. Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are the two most widely used heuristics that have shown their effectiveness in solving such a problem. This paper presents, smartPATH, a new hybrid ACO GA algorithm to solve the global robot path planning problem. The algorithm consists of a combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator for avoiding falling into a local minimum. Our system model incorporates a Wireless Sensor Network (WSN) infrastructure to support the robot navigation, where sensor nodes are used as signposts that help locating the mobile robot, and guide it towards the target location. We found out smartPATH outperforms classical ACO (CACO) and GA algorithms (as defined in the literature without modification) for solving the path planning problem both and Bellman-Ford shortest path method. We demonstrate also that smartPATH reduces the execution time up to 64.9% in comparison with Bellman-Ford exact method and improves the solution quality up to 48.3% in comparison with CACO.

A Heuristic Operator-Based Quantum Ant Colony Optimization Algorithm for Robot Path Planning

“…The first ACO algorithm, called Ant System (AS), was applied to the Traveling Salesman Problem (TSP) by Dorigo M. [1]. It constitutes the main framework of other ACO algorithms and is considered as a prototype.…”

“…And robotic is gaining a lot of interest in our daily life and in several areas including industrial applications, manufacturing and constructions, search, rescue, environment exploration and others [1]. Mobile robots path planning is a critical and fundamental problem in the field of mobile robots, whose main task is finding a collision-free path for the robot starting from its initial location to the target location in a given environment with obstacles while satisfy a set of optimization criteria including traveled distance, processing time and energy consumption.…”

“…During the past years, A number of path planning algorithms such as A*, PSO and ACS [1] have been proposed for use in mobile robots. However, those methods are not enough efficient, poor global search capability, and not enough adaptability to complex spatial domain.…”

Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments

“…The grid-map is divided into cells in which the robot has to traverse a set of free cells to reach its goal. In the literature, several heuristic solutions and approaches have been proposed to solve the global path planning problem such as Ant Colony Optimization (ACO) (Chaari et al 2012), Genetic Algorithms (GA) (Alajlan et al 2013), Particle Swarm Optimization (PSO) (Shiltagh and Jalal 2013) and Tabu Search (TS) (Masehian and Amin-Naseri 2006). Other techniques are based on exact methods, such as Dijkstra and A*, which have the advantage of being complete and guarantee finding the optimal solution if it exists.…”

Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms

“…The path finding problem is typically defined in terms of the Travelling Salesman Problem (TSP) [7] or a more general Vehicle Routing Problem (VRP) [4]. Determining the optimal solution is an NP-hard problem, so the size of problems that can be solved optimally is limited [3]. In the situation of environment monitoring systems, the problem is even more complex because exact positions of the sampling points are not known a priori.…”