Humanoid Robotics Ad-Hoc Network (HURANET) using Kalman Filtering Method

Social humanoid robotic development becomes the futuristic vision to accomplish various tasks where the human cannot deploy. Artificial Intelligence (AI) and Machine Learning (ML), ad hoc network and robotic technologies involved to form a team of social humanoids. Amongst all, to establish an ad hoc network among the humanoids, a state information is required. The novelty of this proposed research is to accumulate the state information to establish a smart ad hoc network among the humanoids robots (HR) by observing the environment through various sensors and interoperable state of other coordinating humanoids within the ad-hoc network boundary. Here, Kalman Filtering Method (KFM) is used for computing the future state of the individual humanoid to accept the request to become a member of the team. Evaluation of new humanoid entry and exit is carried out in terms of state information found in the event database. The proposed model experimented using Dynamic MANET On-demand (DYMO) protocol to establish HURANET and the outcome proves that it performs better.