Petri-net-based implementations for FIRA weightlifting and sprint games with a humanoid robot

In this paper, the Petri net-based wireless sensor node architecture (PN-WSNA) is used to control a humanoid robot to play weightlifting and sprint games in the FIRA HuroCup league. With the PN-WSNA approach, the control scenario and decision-making for playing weightlifting and sprint games can be modeled as a PN-WSNA model. The PN-WSNA inference engine is further used to interpret and execute the PN-WSNA model according to the sensor information from visual perception. Therefore, the implementation of playing weightlifting and sprint games is achieved in terms of the PN-WSNA model instead of native code. To verify the PN-WSNA-based implementation approach, an autonomous humanoid robot equipped with a camera and a single-board computer is used for experiments, where the camera is responsible for grabbing image frames; the single-board computer is responsible for visual localization; and the PN-WSNA models the execution and locomotion command generation. Finally, several PN-WSNA models for playing weightlifting and sprint games are proposed and the experimental results are demonstrated and discussed to validate the feasibility of applying the proposed PN-WSNA-based implementation approach.