A Comparative Study: Modified Particle Swarm Optimization and Modified Genetic Algorithm for Global Mobile Robot Navigation

Abstract

In this paper, Modified Genetic Algorithm (MGA* 1 ) and Modified Particle Swarm Optimization (MPSO* 1 ) are developed to increase the capability of the optimization algorithms for a global path planning which means that environment models have been known already. The proposed algorithms read the map of the environment which expressed by grid model and then creates an optimal or near optimal collision free path. The effectiveness of these optimization algorithms for mobile robot path planning is demonstrated by simulation studies. This paper investigates the application of efficient optimization algorithms, MGA* and MPSO* to the problem of mobile robot navigation.Despite the fact that Genetic Algorithm (GA) has rapid search and high search quality, infeasible paths and high computational cost problems are exist associated with this algorithm. To address these problems, the MGA* is presented. Adaptive population size without selection and mutation operators are used in the proposed algorithm. In this thesis, Distinguish Algorithm (DA) is used to check the paths, whether the path is feasible or not, in order to come out with all feasible paths in the population.Improvements presented in MPSO* are mainly trying to address the problem of premature convergence associated with the original PSO. In the MPSO* an error factor is modelled to ensure that the PSO converges. MPSO* try to address another problem which is the population may contain many infeasible paths. A modified procedure is carrying out in the MPSO* to solve the infeasible path problem.According to the simulation done using MATLAB version R2012 (m-file), both algorithms (MGA* and MPSO*) are tested in different environments and the results are compared. The results demonstrate that these two algorithms have a great potential to solve mobile robot path planning with satisfactory results in terms of minimizing distance and execution time. The simulation results illustrate that the path obtained by MGA* is the shortest path, however the execution time based on MPSO* is significantly smaller than the execution time of MGA*. Thus, the proposed MPSO* is computationally faster than the MGA* in finding optimal path.

Path Planning for Batch Picking of Warehousing and Logistics Robots Based on Modified A* Algorithm

“…and m pickNode reaches the picking point of pickNode and completes the picking task [7]. It is important to note that every time we use A* algorithm to plan the pickNode path of picking point, we need to remember the precursor node of picking point pickNode to determine the posture and position of the robot.…”

“…The third step is to calculate the cumulative probability λ of the individual in the population and the selected probability λi and choose individual to generate new population by means of roulette. The formulas (7) and ( 8) are used to determine the cross probability δ, and generate a new individual. The mutation probability is determined by the formulas ( 9) and ( 10), and the mutant individual is produced.…”

Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

“…In a literature study, more than one sensor type and stored maps are used with fuzzy logic navigation approach for mobile robot [5]. In another study, Hybrid Particle Swarm Optimization(HPSO) was proposed to avoid 5‐DOF robot static obstacles in 2D environment [6].…”