Multi-objective optimization technique for trajectory planning of multi-humanoid robots in cluttered terrain

Humanoid robots hold a decent advantage over wheeled robots because of their ability to mimic human exile. The presented paper proposes a novel strategy for trajectory planning in a cluttered terrain using the hybridized controller modeled on the basis of modified MANFIS (multiple adaptive neuro-fuzzy inference system) and MOSFO (multi-objective sunflower optimization) techniques. The controller works in a two-step mechanism. The input parameters, i.e., obstacle distances and target direction, are first fed to the MANFIS controller, which generates a steering angle in both directions of an obstacle to dodge it. The intermediate steering angles are obtained based on the training model. The final steering angle to avoid obstacles is selected based on the direction of the target and additional obstacles in the path. It is further works as input for the MOSFO technique, which provides the ultimate steering angle. Using the proposed technique, various simulations are carried out in the WEBOT simulator, which shows a deviation under 5% when the results are validated in real-time experiments, revealing the technique to be robust. To resolve the complication of providing preference to the robot during deadlock condition in multi-humanoids system, the dining philosopher controller is implemented. The efficiency of the proposed technique is examined through the comparisons with the default controller of NAO based on toques produces at various joints that present an average improvement of 6.12%, 7.05% and 15.04% in ankle, knee and hip, respectively. It is further compared against the existed navigational strategy in multiple robot systems that also displays an acceptable improvement in travel length. In comparison in reference to the existing controller, the proposed technique emerges to be a clear winner by portraying its superiority.

Particle Swarm Optimization aided PID gait controller design for a humanoid robot

Gait planning for the humanoid robot is a very essential and basic requirement. The humanoid robot is balanced at two feet; therefore, special attention is required for gait analysis for the execution of assigned tasks. In this paper, the linear inverted pendulum (LIPM) model is considered to simplify the study and to obtain better gait planning of humanoid robot NAO. Center of mass (COM) and zero moment point (ZMP) criterion are applied with the LIPM model for a better understanding of selecting the step length and period. In addition, a PSO (particle swarm optimization) tuned PID (proportional-integral-derivative) controller has been implemented. Sensory data such as the location of obstacles and the target along with the desired trajectory aided inverse kinematics have been embedded to the conventional PID controller, which provides an interim angle to start the navigation. This interim angle has been carried forward to the PSO technique accompanied by the desired trajectory. It tunes the parameters of the conventional PID controller and provides an optimum turning angle, which avoids obstacles and increases the stabilization of the robot while crossing it. It reduces travel time and shortens travel length. PSO technique minimizes the computational complexity and number of iteration because it requires fewer tuning parameters. Simulations are executed on the simulated NAO robot for the conventional PID controller and the proposed controller. To ratify its findings, experiments are carried out on a real NAO robot in laboratory conditions for both the conventional PID controller and the proposed controller. Simulation and experimental results are presenting a good agreement among each other with deviation under 6%. Applying the PSO tuned PID controller provides a predictable gait and reduces the stabilization time and essentially eliminating the overshoot by 25%. A comparative study with various controllers is performed, and the credibility of the evaluated result has been examined using statistical analysis. The proposed controller has been compared with a previously developed technique to ensure its robustness.

An O(N) Algorithm for the Computation of the Centroidal Dynamics with Application in the Postural Balance of a Humanoid Robot Using Whole Body Control

In this paper, we introduce an [Formula: see text] algorithm for the computation of the centroidal momentum matrix (CMM) and its time derivative using spatial algebra and expressed with Lie algebra operators. The proposed algorithm is applied to the postural balance of a humanoid robot using whole body control with quadratic programming. The employed tasks only require the CMM and its time derivative without the need of the joint space inertia matrix and the Coriolis terms reducing this way the computational cost of the controller. Finally, four simulation scenarios programmed in Julia are considered where several perturbations for the balance of the robot have been taken into account and according to the tracking graphs of the center of mass, centroidal momentum and the trajectories of the center of pressure it is concluded that the performance of the proposed algorithm is satisfactory.