This is the basic skill of animals in this world who live in a complex environment, but in robotics it is the most difficult problem. Thanks to technological progress, the robot will become an assistant to humans in the near future. Nowadays, robots still cannot move in a complex environment. Mobile robots have many applications: military applications, search and rescue missions where they keep humans out of danger, to the exploration of other planets. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an Original EssayOne of the most important reasons for their ability to do this is that they are precisely that: mobile. Non-mobile robots, like industrial robots, rarely leave factories. There are two ways for a machine to move. The first goes through the wheels, like cars, and the second, through the legs, like animals. Where an ideal legged locomotion machine is defined as one in which "the main body of the robot is modeled as a rigid body, the legs massless and capable of providing unlimited force (but not torque) into the contact surface at the contact points of the feet” which is a good approximation of a legged robot. When one foot is lifted off the ground, the support area becomes much smaller, making the robot much less stable. If the center of mass moves out of the support zone, the robot will fall if it does not adjust its balance, i.e. does not use dynamic stability. A certain margin must be maintained between the center of mass and the edges of the support zone in order to be able to manipulate external forces, such as the inertia forces subjected to the robot if it moves then suddenly stops or when it turns. In order to remain statically stable, the robot must therefore only raise its feet so as not to “shrink” the support area too much. It can only move its center of mass (relative to its support area) a small distance before having to change the support area, which usually means taking a step. Both statically stable and dynamically stable walking robots have shown great capabilities.Artificial neural networkArtificial neural network (ANN) takes its name from the network of nerve cells in the brain. Recently, ANN has emerged as an important technique for classification and optimization problems. Artificial neural networks have emerged as a powerful learning technique to perform complex tasks in highly nonlinear dynamic environments. Some of the main advantages of using ANN models are their ability to learn based on the optimization of an appropriate error function and their excellent performance for approximating a nonlinear function. ANN is capable of performing nonlinear mapping between input and output space due to its large parallel interconnection between different layers and nonlinear processing characteristics. An artificial neuron essentially consists of a computing element that performs the weighted sum of the input signal and the connection. weight. The sum is added with the bias or threshold and the resulting signal then passes through a nonlinear function of the sigmoid or hyperbolic tangent type. Each neuron is associated with three parameters whose learning can be adjusted; these are the connection weights, bias and slope of the nonlinear function. Structurally, a NN can be single-layer or multi-layer. In a multilayer structure, there are one or more artificial neurons in each layer and, for a practical case, there may be several, 67(5), 3585-3593.