Intelligent Approaches in Locomotion - A Review <s> Multi-layer perceptron <s> Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ... <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Multi-layer perceptron <s> Abstract As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method. <s> BIB002 </s> Intelligent Approaches in Locomotion - A Review <s> Multi-layer perceptron <s> This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot. <s> BIB003
In a multi-layer perception (MLP), data is fed forward through layers of neurons. A typical arrangement is to have an input layer, a hidden layer and an output layer, although more hidden layers are possible. Each layer after the input layer can be fully connected to the previous layer. So, a neuron in the first hidden layer takes a weighted sum of the inputs, plus a weighted bias value, and processes that information through an activation/transfer function, which is usually a non-linear, sigmoid type. Similarly, output neurons process a weighted sum of the output of the hidden layer neurons, plus another weighted bias, to produce an output. MLPs are then trained and configured by adjusting their weights, so that the produced output is close to the target. In BIB003 (also mentioned in section 2), two MLPs were used to specify parameters of a bipedal ditch crossing gait. The gait was largely constructed analytically but actuator values for the swing leg were left to be controlled by the NNs. The NNs were trained with GA and the best solution was found to be more stable and efficient than one from a fully analytical approach, and slightly better than the result obtained with a fuzzy logic based method. In BIB001 , BIB002 NNs were trained with a GA derived data set that contained optimal parameter values (for energy minimisation), for an analytical based stair climbing solution. The set had values for different step lengths of the robot and stair heights in the environment. Once trained, the NN gave an effective method to interpolate between the GA derived data, in order to approximate optimal values for new step lengths and stair heights.