Path Planning of a Mobile Robot Using Real-coded Genetic Algorithm Based Simultaneous Exploration

Abstract

In mobile robot researches, path planning and obstacle avoidance plays a very important role and has been a very challenging research topic. For path planning, it should produce continuous path from the starting point to the destination without colliding obstacles. Therefore, we proposes a genetic algorithm to search the path with the shortest path in Labview environment. The difficulty of the genetic algorithms applied to the mobile robot is how to reduce the complexity of the genetic operations, and how to avoid the region optimal solution and adaptation of environmental change. Many researchers studied genetic algorithms to determine the optimal solution such as path planning, but the past literatures mostly used binary coding for the gene encoding. If the path is longer or the number of obstacles is larger, the binary coding will be a lengthy string of series. This will result in the longer evolution of computing time. As a result, we propose a novel method which is a serial number encoded as a gene encoding to effectively reduce the evolution of computing time for the path planning applications.