Pattern-Based Genetic Algorithm Approach to Coverage Path Planning for Mobile Robots

Abstract

Sensor-based mobile robot coverage path planning (MRCPP) problem is a challenging problem in robotic management. We here develop a genetic algorithm (GA) for MRCPP problems. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a path which runs through the center of each disk at least once with minimal cost of full coverage. The proposed GA utilizes prioritized neighborhood-disk information to generate practical and high-quality paths for the mobile robot. Prioritized movement patterns are designed to generate efficient rectilinear coverage paths with no narrow-angle turn; they enable GA to find optimal or near-optimal solutions. The results of GA are compared with a well-known approach called backtracking spiral algorithm (BSA). Experiments are also carried out using P3-DX mobile robots in the laboratory environment.

A genetic algorithm for task completion time minimization for multi-robot sensor-based coverage

“…It has several obstacles to pose a challenge for the proposed methods. A comprehensive comparison of the HOGA on real robots with travel distance criteria can be found in [12] against BSA, and in [11] against STC with satisfactory results.…”

“…The oriented genetic algorithm (OGA) is discussed in details in [12] for travel distance cost criteria. Here, we will outline the algorithm for travel time criteria.…”

“…These compasses that firstly proposed in [7] prioritize neighboring disks and fix a visit pattern for robots in a chromosome. Later, orientation compass concept is extended to eight unique compasses in [12] for distance-based path planning. The approach reduces the search space from finding the visiting order of disks into selecting the use order of orientation compasses for a fullcoverage of the environment.…”

A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

“…Therefore, robots visit disk centroids according to their patterns and their associated number of moves. This approach compresses the chromosome length significantly leading to smaller search space with respect to previous pattern-based GA (Kapanoglu et al 2009). …”

A Bounded Strategy of the Mobile Robot Coverage Path Planning Based on Lorenz Chaotic System

“…It is needed for a variety of applications such as vacuum cleaning robots [2], lawn mowers [3], exploring underwater sources [4], demining robots [5], etc. Different coverage path planning algorithms are proposed for the above applications, such as the backtracking spiral algorithm (BSA) [6], back-andforth method [7,8], as well as some artificial intelligence algorithms, for instance, the neural network [9,10], genetic algorithm [11,12], etc. Each method can be suitable for different applications.…”