Heterogeneous group path planning algorithm based on data and mechanism model

Abstract It is a challenging task to find a feasible path from the start to the end for heterogeneous group and avoid collision between dynamic agents and static obstacles. The existing methods are usually applicable to static simple scenes or the type of scenes including a single kind of agents, and it is difficult to meet the high dynamic heterogeneous group movements. To address the above issues, we propose a hybrid driven heterogeneous group path planning method based on data and mechanism model. Data and mechanism model are combined to drive movements of heterogeneous groups. The experimental results show that our method can describe movements of heterogeneous groups more realistically and solve the collision avoidance of heterogeneous groups well. We quantitatively evaluate our method using metrics such as the number of inflection points and the average turning angle. Average turning angle has decreased by 59.50% on average over prior methods. Number of inflection points has decreased by 69.19% on average over prior methods.