Towards stabilization and navigational analysis of humanoids in complex arena using a hybridized fuzzy embedded PID controller approach

In this study, path planning and stabilization of humanoids are carried out in an uneven path and dynamic environment. The importance of the work focuses on avoiding local minima and trapping in dead-ends during navigation. The sole purposes of this research are to i) Stabilize the humanoid on an uneven surface. ii) Develop proper path planning for the humanoid so that it can adequately navigate through obstacle-rich terrain. For achieving the above said objectives, the robot's controller is designed by tuning the Proportional-integral-derivative (PID) controller with a fuzzy logic controller (FLC) for better performance. The PID controller does joint angle control and torque control respectively. In contrast, a complicated task like generating optimized turning angle (OTA) and adjusting feet angle during stepping upon an uneven path is done by FLC. Gait generation during stepping on and off an uneven surface for the humanoid robot is discussed and implementation of the inverted pendulum plus flywheel method (LIPPFM) is used for the analysis of the dynamics of motion and removing the height constraint (center of mass) where the upper body is considered as the mass of the pendulum. Petri net controller is used to navigate humanoids in an environment with multiple humanoids. To examine the proposed controller’s performance, the controller undergoes testing in simulation and experimental set up, and the obtained results are compared with recently developed techniques. V-REP software is used for conducting the simulation with an arena-size of 240*160 dimensions to test the effectiveness of the developed controller. A less than 5 % deviation is found because of friction and signal delay is noticed between simulation and the experimental result obtained for navigation while comparing to the previously developed technique such as PEM. There is a significant improvement in path length by 10.49 % is noticed and a decrease of 2.98 % in computational time is noticed. When the navigation parameter of the FUZZY-PID controller is compared with Genetic Potential Field (GPF), Pseudo Bacterial Potential Field (PBPF), and Bacterial Potential Field (BPF), there is an improvement of 15.41 %, 12.51 %, and 8.56 % respectively are noticed. It has been seen that the FUZZY-PID controller minimizes the settling time and lower the peak overshoot. When the humanoid robot crosses the uneven path using the proposed FUZZY-PID controller, the graph obtained is smoother than the previously developed technique. It displays the body attitude angle falling between less than ±1.5 degrees compared to ±2 degrees by the previously developed method which justify the selection of the FUZZY-PID controller.