Towards motion planning of humanoids using a fuzzy embedded neural network approach

This research work focuses on navigational strategy of humanoid robots in complex environments using a fuzzy embedded neural network based controller. The obstacle distances are measured from robot's current position and referred as front obstacle distance, right obstacle distance and left obstacle distance. These obstacle distances are served as input variables to the neural network model, and target angle is obtained as output parameter. The target angle obtained from neural network is fed to the Mamdani fuzzy system along with the obstacle distances as input variables to obtain the effective target angle for the humanoid robot. A Petri-net controller is embedded with developed neuro-fuzzy controller to perform dynamic path analysis in complex workspaces Single as well as multiple humanoid robots are used to analyze simulation and experimental navigation in different complex environments using developed neuro-fuzzy-petri-net controller. Various simulations are carried out using V-REP simulation software and similar scenario as per simulation is developed under laboratory conditions for various experimental navigation. The results from both the scenarios are related and are found to be in good covenant with each other having permissible range of errors. Simulation and experimental results in relation to navigational parameters shows the robustness of the developed controller. Surface plots and contour plots developed from the designed controller shows the effectiveness and efficacy in achieving global path during motion planning through optimizing target angle. To validate the results and to find out the effectiveness, the developed controller is compared with existing techniques such as IDQ and substantial progress of 16.66% in relation to path length is observed. (c) 2022 Elsevier B.V. All rights reserved.