Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—This paper presents a solution in path planning for a robotic arm based on the artificial neural network (ANN) architecture, particularly a Static (Feedforward) Neural Network (SNN). The inputs of the network are the sample sets that are obtained from some specific requirements of the desired trajectory. After training, the outputs of the network are the smooth curves that will be used as the reference trajectory for the joints of the excavator arm. The capabilities of the designed neural network in solving the path planning problems are clearly demonstrated through a simulation conducted with a complex trajectory for the excavator.
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