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There are many processes involved in construction, it is necessary to optimize the path planning of construction robots. Most researches focused more on optimization algorithms, but less on comparative analysis based on the advantages and shortcomings of these algorithms. Therefore, the innovation of this paper is to analyze three advanced optimization algorithms (genetic algorithm, hybrid particle swarm algorithm and ant colony algorithm) and discuss how these algorithms can improve the optimization performance by adjusting parameters. Finally, the three algorithms are compared and analyzed to find an optimization algorithm that is suitable for path planning optimization of construction robots. The purpose of the optimization is to obtain the maximum benefit with the least cost and complete project in an efficient and economical way.

To develop intelligent construction robots, the navigation system that can provide efficient path planning algorithm is necessary. The purpose of path planning method for construction robot is to find the shortest and collision-free path from initial position to target position. Koo presented an improved bug-based algorithm which can produce an effective path in an unknown environment with both stationary and movable obstacles. The contributions, which make it possible to generate an effective and short path, are an improved method to select local directions, a reverse mode, and a simple leaving condition [

Soltani also presented a framework based on transportation costs, safety and reliability for construction path planning. He studied a fuzzy-based multi-objective optimization method to make optimal strategic decisions on the movement path of construction site, and make detailed decisions on the distance of the workplace [

Nieuwenhuisen described a new robotic method that can calculate a smooth, collision-free, and high-quality path map. This roadmap can be used to get optimal paths for robots. He also described the application of this technique for planning the movement of entity groups and creating smooth camera movements through the environment [

Lu has developed a practical method to solve the basic problems and limitations of existing resource scheduling methods by using the critical path method (CPM). The proposed method is referred to as the resource activity critical path method (RACPM), where resource dimensions other than activity and time are highlighted in project scheduling. By running RACPM under different confition, we can study the impact of various resources on project, which leads to a comprehensive schedule that provides a timetable for establishing, estimating, and controlling budgets [

Chang developed an automatically and efficiently plan with steps. The first step is to convert the crane installation site into configuration space, including the crane’s load capacity and obstacles. The second step is to find an availability path in configuration space by using the probabilistic roadmap method (PRM). Three tests were conducted in this study to verify the behavior of the proposed method. The results show that the proposed method can produce an effective installation path to operate and is suitable for crane installations, helping engineers to verify planning decisions [

From above review, we can conclude that most researches focused more on optimization algorithms, but less on comparative analysis based on the advantages and disadvantages of these algorithms. Therefore, the innovation of this paper is to analyze three optimization algorithms and discuss how these algorithms can improve the optimization performance by adjusting parameters. In this paper, a specific case has been analyzed, we need to choose a shortest path that goes through all the points. Finally, the three algorithms are compared and analyzed to find an optimization algorithm that is suitable for path planning optimization of construction robots.

In computer science and operations research, genetic algorithm (GA) is a method inspired by the natural selection process and belongs to a larger class of evolutionary algorithms (EA). Genetic algorithms are often used to generate high-quality solutions for optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection [

Evolution usually begins with a randomly generated set of individuals and is an iterative process called generations. In each generation, assess the fitness of every individual in the population. Adaptability is usually the value of the objective function that solves the optimization problem. More suitable individuals are randomly selected from the current population, and each individual’s genome is modified (recombinant and possibly randomly mutated) to form a new generation. Then use the next generation of candidate solutions in the next iteration of the algorithm. Typically, the algorithm terminates when it produces the maximum generations or meets a satisfactory level of fitness.

The first step of genetic algorithm is population initialization. Since the genetic algorithm cannot directly deal with the parameters of problem, the feasible solution to the problem to be solved must be represented as a chromosome or an individual in the genetic space through coding. Common coding methods are grey coding, real coding, and structural coding. The real number coding does not have to be converted numerically, and the genetic algorithm operation can be performed directly on the expression of the solution. This article uses real coding to define each chromosome as real variable. Secondly, the fitness function is criterion to distinguish individual good from bad in a group, and is the only basis for natural selection. Generally, it is obtained by transforming an objective function. This article is to find the maximum value of the function as the individual fitness value. The larger the value of individual function is, the better the fitness is. Thirdly, the selection operation is to select a good individual from the old group with a certain probability to form a new group to multiply next generation of individuals. The probability that the individual is selected is related to the fitness value. The higher the individual fitness is, the greater the probability of being selected is. There are various methods for selecting the genetic algorithm, such as roulette method and competition game method. In this paper, the roulette method is adopted. Fourthly, cross operation refers to randomly selecting two individuals from population and transferring excellent genetic characteristics to substrings through the exchange combination of two chromosomes to generate new excellent individuals. Since individuals use real numbers, the crossover method uses real number method. Finally, the last step of genetic algorithm is mutation operation. The purpose of the mutation operation is to maintain the diversity of the population. The mutation operation selects one individual from the population and mutates to produce a better individual.

In this paper, a specific case is analyzed, we need to choose a shortest path that goes through all the points which are showed in

algorithm works best, and other values will reduce the optimized performance. From

Particle swarm optimization (PSO) originated from simulating the social behavior of birds, which was developed by Kennedy and Eberhart. In particle swarm optimization algorithm, each particle flies in the search space, and its speed is adjusted by its own flight memory and companion’s flight experience. All particles have fitness value determined by fitness function [

In this paper, a specific case is analyzed, we need to choose a shortest path that goes through all the points which are showed in

The ant colony algorithm (ACO) is a method to simulate the behavior of ant foraging. It solves traveling salesman and quadratic distribution problems. Ants are social insects that behave more like group than individual [

and pheromone trails. Randomly place the starting nodes of all ants. The second step is solution construction. Taking into account heuristic information dependence and problem path-tracking advantages, each ant chooses the next node that will not move with probability. Repeat this step until the solution building is complete. The third step is to update the path. The solution is evaluated according to the quality of the solution and stores the pheromone in the solution path. The better the solution is, the greater the amount of pheromone deposition is. The fourth step is the evaporation of pheromones. At the end of the iteration, in order to build a complete solution, the pheromone path is reduced by a constant factor [

In this paper, a specific case is analyzed, we need to choose a shortest path that goes through all the points which are showed in

at an early stage, and then gradually tend to be flat. At this time, the optimal results are close to the optimal solution. Therefore, in order to obtain better optimal results, the max iteration should be increased.

In order to choose suitable algorithm, we need to compare the optimization performance of the three algorithms based on their optimal results and convergence speed. Since the goal of this paper is to select the shortest path for robot construction, the smaller the optimal result is, the better the optimization performance is. Firstly, comparing the optimal results of the three algorithms. The

optimal result of genetic algorithm is 440.818, the optimal result of hybrid particle algorithm is 436.482, and the optimal result of ant colony algorithm is 441.253. It can be concluded that the optimal result of hybrid particle algorithm is the smallest and its optimization performance is the best. Secondly, compare the convergence speed of the three algorithms. From

The optimization of path planning for construction robots will improve the efficiency of construction process and save costs. Many researchers have proposed optimization methods to solve this problem, but there is less research on comparative analysis based on the advantages and shortcomings of these algorithms. Therefore, this paper analyzes the three optimization algorithms and discusses how these algorithms can improve the optimization performance by adjusting parameters. Finally, the three algorithms are compared and analyzed to find an optimization algorithm that is suitable for solving this problem. Based on above

analysis, this paper recommends to use hybrid particle algorithm to solve the problem of path optimization for construction robots.

Tan, K. (2018) Optimization of Path Planning for Construction Robots Based on Multiple Advanced Algorithms. Journal of Computer and Communications, 6, 1-13. https://doi.org/10.4236/jcc.2018.67001