Simulation of swarm algorithms for path planning in a two-dimensional non-mapped environment

This paper examines the effectiveness of swarm path planning algorithms in a two-dimensional unmapped environment. The efficiency criteria are the number of iterations in the path finding process and an assessment of the probability of successfully achieving the goal. During the study, the maximum speed of movement of the swarm and the maximum number of iterations during which it is allowed that the distance to the target does not decrease are changed. It is assumed that each particle can determine the state of the environment in a certain local region. By determining the state we mean determining the presence of an obstacle in a cell of the environment. To solve the problem of local minima, it is proposed to introduce a virtual obstacle at the local minimum point. This approach is generally known. The novelty of this approach lies in the fact that it solves the problem of detecting a local minimum by a swarm of particles. With a single movement, detecting a local minimum is trivial and comes down to checking the movement to previously visited cells. In the group case, a new solution to the problem of detecting a local minimum is required. This article provides a review and analysis of the path planning problem, problem formulation, problem statement, mathematical description of global swarm path planning algorithms with proposed modifications, pseudo-codes of planning algorithms and the results of a numerical study. In the course of numerical studies, the paper presents the criteria for the efficiency of path planning in an environment of 100$\times$100 cells with randomly placed obstacles.