Multi-objective trajectory planning of humanoid robot using hybrid controller for multi-target problem in complex terrain

Humanoid robotics is an emerging area of interest in the current engineering research scenario, owing to its ability to impersonate human deportment and emulate various jobs. The given article emphasizes the development and implementation of a hybrid navigational controller to optimize the path length, energy demand, and time spent for accomplishing assigned tasks. The proposed navigational controller is developed by hybridizing the metaheuristic Improved Spider Monkey Optimization (ISMO) approach and the Regression Analysis (RA) approach. Various input parameters like obstacle and target locations are fed to the RA approach that implements a proper navigational direction selection. And it forwards to the SMO approach that is improved using piecewise B-Spline path smoother, which exercises further refinement of the output turning angle and smoothness of path around obstacles. Simulations and real-time experiments are undertaken using different controllers involving single robot systems, which shows the proposed controller's superiority. An average improvement of 13.72% and 13.94% in path length against RA in simulation and experiment, respectively, and an average improvement of 7.59% and 7.5% in path length against ISMO in simulation and experiment, respectively, is obtained. It is further evaluated for navigation by implementing in a single robot having a multi-target problem. Multiple robot navigation has to deal with the self-collision situations that are solved by prioritizing the specified robot using the dining philosopher controller. It is implemented in the proposed controller for navigation of multiple robots to solve the conflict. Both scenarios are tested in the simulation environment and ratified in the experimental environment. Average deviation under 5% for path length and time spent for single robot navigation and multiple robot navigation is obtained, which shows a good agreement with each other. Energy efficiency test has been performed in contrast to default controller of NAO for various joints, and an average improvement of 8.16%, 5.9% and 20.57%, has been recorded in torque for ankle, knee and hip, respectively. Comparison is carried with an established navigational controller in a similar environmental setup shows an improvement of 8.6% and 10.365%, respectively, in path length and time spent. The results obtained from these setups prove the proposed hybrid controller to be robust, efficient and superior while performing path planning.