International Journal of Humanoid Robotics

This study aimed to propose an extended state observer fuzzy-approximation-based active disturbances rejection control (FAADRC) method for a dual-arm humanoid robotic system. The purpose of this control system was to provide disturbance estimation and compensation to enable the humanoid robots to track any continuous desired trajectory, even in the presence of environmental disturbances and parametric uncertainties. The proposed active disturbances rejection controller was analyzed using mathematical modeling, and the robot dual-arm motion information of a number of cases when they simulated the trajectory was examined to verify the model. The extended state observer adaptive fuzzy-approximation control strategy was designed combining the synthesis of the robust design, active disturbances rejection control, and Lyapunov function method so that the proposed FAADRC did not need to know the arms model of the humanoid robot precisely. In the control system proposed in this study, once the desired trajectories of the robot’s dual-arm positions were given, the FAADRC system was closed to any unknown functions and to the derivative of the virtual control law of the humanoid robot system. In this case, a robust controller based on an extended state observer was designed to realize the disturbance estimation and compensation. Using the proposed trajectory tracking, not only were the coordinate motions of a humanoid robot’s two arms generated, but the arms could also be controlled to move to the desired positions. The proposed closed-loop system under the FAADRC design was effective, and the asymptotic stability was successfully achieved. The numerical simulation showed the tracking error comparison and the estimated errors of the extended state observer. Two experimental tests were carried out to prove the performance of the algorithm presented in this study. The experimental results showed that the proposed FAADRC exhibited a better performance than the regular proportional integral derivative controller.

The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.

This paper describes a rule base-Sugeno fuzzy hybrid controller for path planning of single as well as multiple humanoid robots in cluttered environments. Initially, sensor outputs regarding the obstacle distances are used as inputs to the rule base model, and turning angle is obtained as the output. The rule-based analysis is used for training the fuzzy controller with membership functions. The output from the rule base model along with other regular inputs is supplied to a Sugeno fuzzy model, and effective turning angle is obtained as the final output to avoid the obstacles present in the environment and navigate the humanoids safely to their target points. The proposed hybrid controller is tested on a V-REP simulation platform, and the simulation results are validated in an experimental set-up. To avoid the possibility of any inter-collision during navigation of multiple humanoids on a common platform, a Petri-net scheme is integrated along with the proposed hybrid model. Finally, the results obtained from simulation and experimental platforms are compared against each other with proper agreement and minimal percentage of deviations. To validate the proposed controller, it has also been tested against another existing navigational approach, and satisfactory performance enhancement has been observed.