Mines are considered to be one of the most valuable assets of a country, and mining activities are key factors in creating productive employment and increasing the gross national product. Hence, increasing productivity and improving the efficiency of transportation systems in mines are vital. Also, the mine environment is dangerous; therefore, one way to achieve the aforementioned goals and reduce human casualties is to use autonomous trucks. In recent years, there have been many activities toward autonomous cars, but not so much studies on autonomous dump trucks. Generally, these trucks identify their surroundings by using sensors such as radars, lidars, ultrasonic distance meters, and cameras. Then, using different control algorithms, they decide to move in different directions. An autonomous car is an unmanned vehicle that can identify its environment and move without human aid. Autonomous driving is achieved in five stages; the first one involves control via a driver, and in the last one, the car is totally independent of the driver. To make a vehicle autonomous, different parts play different tasks. These tasks include path planning and selection, online controlling of the car, avoiding hitting obstacles, and guaranteeing the safety of the car and its passengers. The focus of this research is to develop better control mechanisms for cars, with a specific emphasis on mining trucks. To simulate the mining environment, a model similar to the truck model was used. In order to implement the controller on the Sarcheshmeh mine trucks, it is necessary to simulate different methods and choose the best one. To achieve this goal, geometric controllers such as Pure Pursuit and Stanley are simulated. Then, a PID controller, one of the most important practical controllers, is applied. Also, the PID controller is used for controlling the speed, or longitudinal control. To test and eva‎luate model-based controllers, a transverse and dynamic model for the truck has to be extracted. This model is under the effect of a non-linear tire model. The extraction of this model is based on Newton’s laws, which are addressed in this research. One of the model-based controllers selected in recent studies and in demand by researchers is the predictive controller. This controller is also designed and implemented in the simulator.

ايمان ايزدي نجف آبادي

مرضيه كمالي , حامد جلالي بيدگلي