Dynamic Path Planning Algorithm for a Mobile Robot Based on Visible Space and an Improved Genetic Algorithm

Abstract

In this study, a series of new concepts and improved genetic operators of a genetic algorithm (GA) was proposed and applied to solve mobile robot (MR) path planning problems in dynamic environments. The proposed method has two superiorities: fast convergence towards the global optimum and the feasibility of all solutions in the population. Path planning aims to provide an optimal path from a starting location to a target location, preventing collision or so-called obstacle avoidance. Although GAs have been widely used in optimization problems and can obtain good results, conventional GAs have some weaknesses in an obstacle environment, such as infeasible paths. The main ideas in this paper are visible space, matrix coding and new mutation operators. In order to demonstrate the superiority of this method, three different obstacle environments have been used and an experiment is conducted. This algorithm is effective in both static and dynamic environments.

Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots

“…To compare the results with other works in the literature, the DPNA-GA was simulated with environments used in works presented in Refs. [9,13,14,17,21]. Figure 13, Figure 14, Figure 15 and Figure 16 show the displacement of the DPNA-GA in the environment proposed in Refs.…”

“…In all cases, a priori knowledge is required of the environment, which is represented using a bidimensional grid. Several of the proposed techniques are specific to static environments [1,4,5,6,7,9], while the proposal presented in Refs. [3,8] is aimed at dynamic environments, although an external observation device is needed to transmit the state of the environment to the robot at a speed faster than the speed of changes in the environment.…”

“…The works presented in Refs. [14,15] propose global navigation planning, similar to the works [1,4,5,6,7,9] with other population-based metaheuristics algorithms [16]. The research [14] uses the ant colony optimization, and the work [15] uses the pseudo-bacterial genetic algorithm.…”

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

“…However, the GA is a very important technique in combinatorial optimization issues [22,23]. The objective of such algorithm in mobile robot (MR) field is to search for the optimal path and trajectory.…”

“…The objective of such algorithm in mobile robot (MR) field is to search for the optimal path and trajectory. In [22], the authors developed the genetic operators for GA to be able to solve path planning problems. They claimed that their proposed algorithm has fast convergence towards the global optimum.…”