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Online Control for Biped Robot with Incremental Learning Mechanism. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188599] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, we develop a new online walking controller for biped robots, which integrates a neural-network estimator and an incremental learning mechanism to improve the control performance in dynamic environment. With the aid of an iteration algorithm for updating, some newly incoming data can be used straightforwardly to update into the original well-trained model, in order to avoid a time-consuming retraining procedure. On the other hand, how to maintain the zero-moment-point stability and counteract the effect of yaw moment simultaneously is also a key technical problem to be addressed. To this end, an interval type-2 fuzzy weight identifier is newly developed, which assigns weight for each walking sample to deal with the imbalanced distribution problem of training data. The effectiveness of the proposed control scheme has been verified through a full-dynamics simulation and a practical robot experiment.
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Rath AK, Parhi DR, Das HC, Kumar PB, Muni MK, Salony K. Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm. INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2019. [DOI: 10.1108/ijius-11-2018-0032] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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.
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