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To navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.

The experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.

From the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.

With a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

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.

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.

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.

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.

With enhanced use of humanoids in demanding sectors of industrial automation and smart manufacturing, navigation and path planning of humanoid forms have become the centre of attraction for robotics practitioners. This paper aims to focus on the development and implementation of a hybrid intelligent methodology to generate an optimal path for humanoid robots using regression analysis, adaptive particle swarm optimization and adaptive ant colony optimization techniques.

Sensory information regarding obstacle distances are fed to the regression controller, and an interim turning angle is obtained as the initial output. Adaptive particle swarm optimization technique is used to tune the governing parameter of adaptive ant colony optimization technique. The final output is generated by using the initial output of regression controller and tuned parameter from adaptive particle swarm optimization as inputs to the adaptive ant colony optimization technique along with other regular inputs. The final turning angle calculated from the hybrid controller is subsequently used by the humanoids to negotiate with obstacles present in the environment.

As the current investigation deals with the navigational analysis of single as well as multiple humanoids, a Petri-Net model has been combined with the proposed hybrid controller to avoid inter-collision that may happen in navigation of multiple humanoids. The hybridized controller is tested in simulation and experimental platforms with comparison of navigational parameters. The results obtained from both the platforms are found to be in coherence with each other. Finally, an assessment of the current technique with other existing navigational model reveals a performance improvement.

The proposed hybrid controller provides satisfactory results for navigational analysis of single as well as multiple humanoids. However, the developed hybrid scheme can also be attempted with use of other smart algorithms.

Humanoid navigation is the present talk of the town, as its use is widespread to multiple sectors such as industrial automation, medical assistance, manufacturing sectors and entertainment. It can also be used in space and defence applications.

This approach towards path planning can be very much helpful for navigating multiple forms of humanoids to assist in daily life needs of older adults and can also be a friendly tool for children.

Humanoid navigation has always been tricky and challenging. In the current work, a novel hybrid methodology of navigational analysis has been proposed for single and multiple humanoid robots, which is rarely reported in the existing literature. The developed navigational plan is verified through testing in simulation and experimental platforms. The results obtained from both the platforms are assessed against each other in terms of selected navigational parameters with observation of minimal error limits and close agreement. Finally, the proposed hybrid scheme is also evaluated against other existing navigational models, and significant performance improvements have been observed.

Being an interdisciplinary research area, biomechanics has gained interest among researchers. Biomechanics deals with integration of mechanical phenomenon with the structural and functional aspects of biological systems. Biological systems being very much complex provide a very intricate platform for their analysis. In case of damages created by accidents or sport malfunctions, artificial implants are used for the replacement of bones. These implants may cause incompatibility with the human body, depending on their design and characterization. So, this research aims to analyze the vibrational characteristics of a human femur bone and to predict the safe ranges of frequencies of operation.

The current research is aimed at vibrational characterization of a human femur bone. The model of the femur bone is prepared using SOLIDWORKS software. The material properties of the femur are collected from the available literature and provided with the CAD model. The model is imported to the ANSYS software. Loading patterns as applied on the human body are also applied to the prepared model. Suitable boundary conditions are chosen for normal sitting and standing positions. The natural frequencies of the femur bone and other vibrational parameters are found out.

The first data obtained from the ANSYS software are the natural frequencies and mode shapes of vibration. Other data include the stress distributions, strain distributions, deformation patterns and potential zones of damage. The frequencies and mode shapes enable the safe ranges of human operation and the frequency range to be followed in the designing of implants. The stress distributions enable to know the potential zones of damage so that those areas can be given focus during strength considerations.

The current investigations take into account only normal sitting and walking conditions. This work can be included under static loadings. This can also be extended toward dynamic loading conditions. In the dynamic loading, walking and running conditions can be taken into account. This work focuses on the safe designing of the artificial implants and their compatibility with the human body. This can also be extended toward role of dynamic forces in the damaged bone formation and the role of implant’s characteristics for healing of bones.

Bone damage and ligament fracture are common nowadays due to increasing number of accidents, which may be vehicular or sports. In case of any damage to the skeletal parts, some artificial implant is used to support the damaged part and to help in the process of healing. The designing of the implants must be compatible with the human body. The natural frequencies and mode shapes give an idea that the vibrational parameters of the implant material must fall in the same range as the actual bone. The stress distribution and potential zone damage emphasize on strength considerations.

The current method is a novel approach toward implant designing. Here an analysis of vibrational parameters of the human femur bone is performed. Those parameters include natural frequencies, mode shapes, principal normal stress distributions, principal shear stress distributions, maximum shear elastic strains and total deformation. These parameters reflect an idea about behavior of the femur bone under actual loading conditions. This analysis enables an implant designer to focus on material properties and strength considerations of the implants which are to be used in case of bone damage.