Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

Abstract

Purpose Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

A Genetic Algorithm-Based Approach for Three-Phase Fault Evaluation in a Distribution Network

“…The genetic algorithm searches for solutions based on the principles of natural selection [30]. The genetic algorithm works as a multipath algorithm, searching multiple peaks simultaneously and in parallel, thereby decreasing the risk of trapping in local minima [31]. It functions by developing codes for the values and evaluates the fitness of every string.…”

“…Perfect mutation is needed to avoid the loss of genetic material. When crossover does not guarantee access to all the desired search spaces, random gene changes through mutation will assist in providing variations in the population [31].…”

“…Since the genetic algorithm works as a multipath search algorithm, the above-mentioned changes would ensure search efficiency until termination of the optimisation processes [21]. This would greatly decrease the chances of local minima trapping [31]. This would also help the algorithm to effectively explore all the search spaces where the probability of finding optimum solutions is highest [29].…”

“…It sometimes converges to local minima. In optimisation cases, an algorithm that converges poorly and settles to local minima is regarded as inaccurate and unreliable [21,31]. That particular algorithm must not be given much priority with regards to optimising much more sophisticated problems [13].…”

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

“…Rath et al 2,3 developed a fuzzy controller for navigation of a humanoid robot with avoiding obstacles. In this research, they provided the obstacle distances from robot and bearing angle as input to the controller and obtained respective velocity of robot to avoid obstacles.…”