Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
Tham khảo tài liệu 'evolutionary robotics part 6', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 192 Frontiers in Evolutionary Robotics We denote by Srt the algorithm which operates the robot with soft-real time considerations influencing his behavior to avoid a dangerous battery level. This algorithm differs from Nrt mainly by battery level influences robot s fitness evaluation used by the GA and a new input neuron is connected to a battery level sensor. Finally we denote by Hrt the hybrid algorithm which operates the robot with hard-real time considerations i.e. the same as Srt incorporating critical battery level sensing and also having the capacity to change the robot s normal operation to mission oriented guaranteeing his survivability if at least one charging zone was previously found . 4.1 Experimental Setup As mentioned before the experiments are performed using a modified version of yaks. This simulation system has several different elements including the robot simulator neural networks GA and fuzzy logic based fitness. Khepera Robot For these simulations a Khepera robot was chosen. The robot configuration has two DC motors and eight six front and two back infrared proximity sensors used to detect nearby obstacles. These sensors provide 10 bit output values with 5 random noise which allow the robot to know in approximate form the distance to local obstacles. The yaks simulator provides the readings for the robot sensors according to the robot position and the map room it is in. The simulator also has information for the different areas that the robot visits and the various obstacles walls or zones home charging zones detected in the room. In order to navigate the robot executes up to 1000 steps in each simulation but not every step produces forward motion as some only rotate the robot. If the robot has no more energy it freezes and the simulation stops. Artificial Neural Network The original neural network NN used has eight input neurons connected to the infrared sensors five neurons in the hidden layer and two output neurons directly connected to the
