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Purpose This paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles. Design/methodology/approach The three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal. Findings Graphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture. Originality/value This paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

The prime challenge in a humanoid robot is its stability on two feet due to the presence of an underactuated system. In this paper, the complete dynamics of the humanoid robot has been described in essence of torque calculation at the end effectors. Presence of various restraints in humanoid robot motion makes the task of stabilization an even humongous one. Therefore, to neutralize these constraints, whole-body control (WBC) has been proposed to consider the free-floating base and to ensure the stability of the humanoid robot. Dynamic modeling of the humanoid robot is performed based on the Langrage–Euler formalism to obtain the maximum torque at the joints. This approach is utilized to formulate the torque equation and solve the problem of stabilization. WBC deals with the limitation of attainment of well nimble dynamics behavior operated at high speeds. The simulated annealing approach is preferred to tune WBC to get efficient stabilization and eliminate the earlier limitation. In addition, the zero-moment point (ZMP) criterion is taken care of as it affects the stability of the humanoid robot aggressively. Simulations on V-REP are carried out to understand the torque behavior at each joint. To validate the simulation results, the experiments are carried out on the NAO humanoid robot in real experimental conditions. The experimental and simulation results are compared through torque versus time graphs, and they both show good agreement with deviation under 4% between them. The proposed technique is then compared with various previously implemented techniques which confirm the robustness and efficiency of the proposed methodology.

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Fuzzy logic is widely known as a value-based technique. Whale optimization algorithm (WOA), on the other hand, is a nature-inspired optimization technique. Hybridization of these two techniques is proposed for path planning and control, over multiple mobile robots in static and dynamic environments. The effectiveness of the resulting technique, known as ‘Fuzzy-WOA’, is tested through MATLAB simulation coupled with real-time experiments. Upon testing, a good agreement is observed between these platforms. Furthermore, the proposed technique is found to be more efficient when compared to other existing techniques, with a significant improvement of about 20.63% in terms of path lengths.