This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.

This paper presents a new combined 9-DOF [3R-3R-3R] parallel architecture to perform the object catching in the real-time scenario. The architecture design is intended to carry heavy payload objects for the catching. This paper covers modelling the new architecture, kinematics, and dynamic analyses to compute torques/forces at the actuators. The simulation of kinematic and dynamic analysis in MATLAB. Vision sensors, encoders, PID controller, and current limiting are used to perform object catching. The architecture is the only delta robot application designed to catch regular-shaped objects in real-time scenarios.

This paper proposes a vision-based kinematic analysis and kinematic parameters identification of the proposed architecture, designed to perform the object catching in the real-time scenario. For performing the inverse kinematics, precise estimation of the link lengths and other parameters needs to be present. Kinematic identification of Delta based upon Model10 implicit model with ten parameters using the iterative least square method is implemented. The loop closure implicit equations have been modelled. In this paper, a vision-based kinematic analysis of the Delta robots to do the catching is discussed. A predefined library of ArUco is used to get a unique solution of the kinematics of the moving platform with respect to the fixed base. The re-projection error while doing the calibration in the vision sensor module is 0.10 pixels. Proposed architecture interfaced with the hardware using the PID controller. Encoders are quadrature and have a resolution of 0.15 degrees embedded in the experimental setup to make the system closed-loop (acting as feedback unit).

The 3 degree-of-freedom Gantry-Tau manipulator with the addition of the spherical wrist mechanism which is called Gantry-Tau-3R is designed as a high-G simulation-based motion platform (SBMP) with the capability of generating the large linear and angular displacement. The combination of both parallel and serial manipulator in newly designed Gantry-Tau-3R mechanism improves the ability of the mechanism to regenerate larger motion signals with higher linear acceleration and angular velocity. The high-frequency signals are reproduced using the parallel part of the mechanism, and sustainable low-frequency accelerations are regenerated via the serial part due to the larger rotational motion capability, which will be used through motion cueing algorithm tilt coordination channel. The proportional integral derivative (PID) and fuzzy incremental controller (FIC) are developed for the proposed mechanism to show the high path tracking performance as a motion platform. FIC reduces the motion tracking error of the newly designed Gantry-Tau-3R and increases the motion fidelity for the users of the proposed SBMP. The proposed method is implemented using Matlab/Simulink software. Finally, the results demonstrate the accurate motion signal generation using linear model predictive motion cues with a fuzzy controller, which is not possible using the common parallel and serial manipulators.

This paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.

This paper presents closed loop simulation of a CMOS-MEMS force sensor for biomedicalapplications employing an optimal proportional-integral-derivative controller. Since thedynamic behavior of the sensor under investigation is nonlinear the iterative feedbacktuning approach was proposed for optimal gains tuning of the proposed controller.Simulation results presented in this research illustrate that the proposed controllersuppresses the undesired in-plane vibration induced by environment or gripper 40 timesfaster than the nonlinear controller proposed in the literature. To suppress the maximuminput disturbance the maximum voltage was approximately 18 V which was less than thepull-in voltage of 30 V. The proposed controller is served to actuate two stator fingersadjacent to a rotor finger in order to provide both the attractive and repellent forcesduring manipulation. Employing the proposed mechanism not only resolves the drawbackscorresponding to the nonlinear controller presented in the literature but also improvesits performance of the closed loop system by using the complete nonlinear dynamics of theforce sensor. Also, applying complete non-linear dynamic of model improves the performanceof controller and is one of superior features of proposed PID controller in comparisonwith the classical controller presented in literature.

This paper addresses the link position setpoint control problem of n–link robotic manipulators with amplitude-limited control inputs. We design a global-asymptotic exact model knowledge controller and a semi-global asymptotic controller which adapts for parametric uncertainty. Explicit bounds for these controllers can be determined; hence, the required input torque can be calculated a priori so that actuator saturation can be avoided. We also illustrate how the proposed control algorithm in this paper can be slightly modified to produce a proportional-integral-derivative (PID) controller which contains a saturated integral term. Experimental results are provided to illustrate the improved performance of the proposed control strategy over a standard adaptive controller that has been artificially limited to account for torque saturation.