It is the basic ability for animals in this world living in complex environment, but in robotics it is the most difficult problem. Thanks to technological advances, in the near future the robot will become an assistant to man. Nowadays robots still cannot move in a complex environment. Mobile robots have many applications: from military applications, to search and rescue missions where they keep humans out of harm's way, to exploring other planets. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay One of the most important reasons for their ability to do this is that they are exactly that: mobile. Non-mobile robots, such as industrial robots, rarely leave the factory. There are two ways a machine can achieve motion. The first is through the wheels, like cars and the second is through the legs, like animals. Where an ideal legged locomotion machine is defined as a machine in which "the main body of the robot is modeled as a rigid body, the legs are massless and capable of providing unlimited force (but no torque) to the surface of contact at the contact points of the feet" which is a good approximation of a legged robot. When you lift one foot off the ground, the support area becomes much smaller, which makes the robot much less stable. If the center of mass goes outside the support area, the robot will fall if it does not adjust its balance, i.e., does not use dynamic stability. It is necessary to maintain a certain margin between the center of mass and the edges of the support area in order to handle external forces, such as the inertia forces that the robot is subjected to if it moves and then suddenly stops or when it turns. To remain statically stable, the robot will therefore have to lift only its feet so as not to "narrow" 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 Network The artificial neural network (ANN) is named after 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 for performing 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 nonlinear function approximation. The ANN is capable of nonlinear mapping between the input and output space due to its large parallel interconnection between different layers and nonlinear processing characteristics. An artificial neuron basically 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 is then passed through a nonlinear function of the sigmoid or hyperbolic tangent type. Each neuron is associated with three parameters whose learning can be regulated; these are the link weights, bias, and slope of the nonlinear function. From a structural point of view, a NN can be monolayer or multilayer. In multilayer structure, there are one or more artificial neurons in each layer and for a practical case there can be multiple layers. Every neuron in a layer is connected to every single neuron, 67(5), 3585-3593.