When developing algorithms for real-time robot path planning, the problem of performance limitations of the corresponding classical algorithms arises. This paper considers a method for planning robot movements in a two-dimensional complex conflict environment. For planning in complex environments, a hybrid planning algorithm is proposed, based on a combination and synthesis of the classical cellular decomposition algorithm and a recently proposed algorithm based on the characteristic visibility graph. This algorithm involves a preliminary analysis of the complexity of the obstacle scene, based on the results of which one of the two specified particular algorithms is selected. It is shown that this approach can significantly overcome the limitations of both of these algorithms. A disturbance avoidance method based on the apparatus of characteristic probability functions is described in a compact form, and its relationship with planning methods in complex environments is demonstrated when solving corresponding problems of global optimization of the probability of successful completion of a target trajectory. The developed approach examines the relationship between the probability of successful path completion in a source field and the corresponding risk function. To solve global robot motion planning problems in complex conflict environments, the proposed hybrid algorithm is first proposed for constructing a family of initial curves within the appropriate feasible motion corridors, ignoring sources. A family of local optimization problems is then solved within the feasible motion corridors, taking sources into account. Next, the trajectory with the maximum probability of successful completion or the normalized safe motion function is selected
