Yerel Benzetim Modellerinde Kalabalığın Genel Gezinim Bilgisinin Kullanım Biçimlerinin ve Katkılarının Araştırılması
Özet
Crowd navigation is one of the quite challenging problems in crowd simulation. Local navigation methods that consider each agent in crowd individually and plan their short term movements have been initially proposed for the navigation problem. Later, global navigation methods that approach the crowd as a whole and hybrid navigation methods that aim combining successful elements of these two types have been developed. Although hybrid navigation methods are more costly as they need to do more calculations, provided that the cost can be kept at a reasonable degree they can produce much more successful results than local and global methods.
Performance of navigation methods is dependent on the agents in simulation to reach their destinations by making the least effort possible. For this purpose, congestions that are caused by agents who try to move in different directions should be minimalized and the crowd should move in a flow. To achieve this goal global path plans of agents should to be made by considering not only the static obstacles in the simulation environment but also other agents’ instantaneous positions and their future plans.
Within this thesis study two new navigation methods have been proposed in which the global navigation information is extracted and stored on simulation environment in a way to include their movement direction and then used by other agents in global path planning phase.
In the Global Path Planning Using Potential Information Method, which is the first one of these methods, global navigation information is represented by potential values. Certain heuristic decisions are made while global path plans are being transformed to potential information and while this information is being used in global path planning. Global path plans that have been made are used in combination with a local navigation method. Comparative tests with a system that uses only a local navigation method have shown that utilizing global navigation information as a guide in local navigation method improves navigation performance remarkably. Besides, this method considerably reduces operation cost of the local navigation method as it minimalizes number of possible collisions by creating a flow in simulation environment.
Second method that is developed is the Time Based Global Path Planning Method. Since global path plans are made on time basis in this method, both extraction of global navigation information of the crowd and usage of this information are carried out in a much more deterministic way in comparison to the potential based method. Machine learning methods are utilized to be able to make global path plans on time basis.
Learning data to be used in machine learning methods is collected from a micro simulation environment and then the models that are trained by using whole of this data are tested on simulation scenarios in medium and macro scale. Therefore a new application schema that differs from traditional machine learning approach about the way learning based methods are used in global path planning is also proposed within this thesis study.
Performance of the time based method is evaluated by comparative tests with the potential based method. Results show that time based method is a better navigation system although it requires slightly more processing power.
