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IntroductionMobile robots have been successfully applied in many areas such as medical and military applications, space exploration, public and domestic duties. They can perform difficult and hazardous tasks with complex requirements and often have to do so autonomously, without the aid of a human operator. To function in that manner they must be able to navigate the environment they are placed in.Collision-free path planning plays an important role in mobile robots navigation and is often a fundamental requirement for proper task execution. The main goal of such planning in an environment with static (stationary) and dynamic (moving) obstacles is to find a suitable movement path from a starting location to a destination, while avoiding the collision with any of these objects. This complex task poses many difficulties: computational complexity, adaptation to changing environment and determining a reasonable evaluation function for the generated path.Path planning is an active research area and many methods have been developed to deal with this problem. They can be classified into classical and heuristic based search algorithms [1], mainly discerned by the type of optimization techniques utilized.Recently some classical approaches, such as cell decomposition [2], potential field method [3][4][5], road map [6] and sub goal network have been presented in the field of mobile robotics. In a cell decomposition method a two-dimensional map is divided into several grids and the path is created in them. Another case of a classical approach is a potential field method in which the controlled robot is attracted by the destination while simultaneously being repelled by the obstacles.These path planning algorithms suffer from some drawbacks [1], e.g., a solution may not be optimal because the algorithm gets stuck in local minima or a new solution has to be generated again when the environment changes and therefore the original path can become infeasible.As a result, many heuristic based methods, such as fuzzy logic [7], artificial neural network [8], nature inspired algorithms [9-12] and hybrid algorithms were created. These methods can overcome drawbacks of the classical ones, but they do not guarantee to find the best solution. Still, the result can be sufficiently close to the optimal one. In this paper the authors used one of those methods -the Particle Swarm Optimization algorithm; it serves as a base solver for collision-free path planning problem.Particle Swarm Optimization (PSO) is a metaheuristic algorithm which is inspired by the social foraging behavior of some animals such as bird flocking and fish schooling. It was developed by Kennedy and Eberhart in 1995 and its description is presented in [13]. Since then, many approaches have been suggested by the researches to solve the collision-free path planning problem using the PSO algorithm [11,[14][15][16][17].Further sections of this paper are arranged as follows: Sect...
