Navigational Analysis of Multiple Humanoids Using a Hybridized Rule Base-Sugeno Fuzzy Controller

Abstract

<jats:p> 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. </jats:p>

Tailoring the motion planning of humanoids in complex arena: a regression-firefly-based approach

“…Kumar et al [25] perform navigational analysis using their developed intelligent motion planner on a humanoid robot. Muni et al [26][27][28][29][30][31] introduced and successfully implemented various artificial intelligence algorithms toward motion planning analysis of legged robots in obstacle-prone environments.…”

Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm

“…Kumar et al 33 have presented hybrid controller of sine-cosine and ant colony algorithm for path optimization. Muni et al [34][35][36] have presented different AI technique, hybridized with fuzzy controller for navigational control of humanoid robots. Kumar 37 have presented hybrid regression fuzzy controller for navigation of humanoid robots.…”

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

“…The goals of optimization involved are the length of the path, the degree of evenness and the degree of safety of the route. Muni et al (2020a, 2020b, 2020c, 2020d, 2021) have proposed the hybrid artificial intelligent technique for control over humanoid robots. Kumar et al (2020a, 2020b, 2021) have proposed hybrid sine cosine and ACO technique path planning of mobile robot.…”

“…The goals of optimization involved are the length of the path, the degree of evenness and the degree of safety of the route. Muni et al (2020aMuni et al ( , 2020bMuni et al ( , 2020cMuni et al ( , 2020dMuni et al ( , 2021 have proposed the hybrid artificial intelligent technique for control over humanoid robots. Kumar et al (2020aKumar et al ( , 2020bKumar et al ( , 2021 Several techniques studied in the literature survey are already proposed by the researchers concerning navigational problems of the mobile robot, but no one confirms the generated path is optimal or not.…”