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In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment. [ABSTRACT FROM AUTHOR]

An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network. [ABSTRACT FROM AUTHOR]

Over the past few decades, research in humanoid robots has been amplified rapidly. This paper displays the attainment of an optimum steering angle to avoid hurdles and reach the target with minimum effort. To achieve this objective, three steps optimization procedure is considered. A hybridization of regression analysis (RA), cell decomposition (CD), and whale optimization algorithm (WOA) are designed and implemented in humanoid NAO for prime trajectory with the least computational cost. RA supplies the first set of steering angles to CD based on the training in the given workspace. CD provides a second set of steering angles to WOA, which results in an optimum steering angle based on its characteristics of hunting prey. The proposed RA-CD-WOA algorithm is evaluated in simulated and experimental workspaces for a single NAO. The proposed algorithm and its standalone algorithms are compared for several iterations, demonstrating the requirement for hybridization. It is also examined for multiple NAOs on a common platform that may lead to a deadlock condition during navigation. To elude this condition, the dining philosopher controller is integrated with the base algorithm, which results in prioritizing a NAO and solving the problem. Further, the RA-CD-WOA algorithm is compared with an existing technique that displays its robustness and effectiveness for robot navigation. • Design of hybrid technique to make the humanoid robot autonomous. • Validation of trajectory planning results of multi-humanoids from simulation using experiments. • Implementation of dining philosopher controller for solving conflicting situations. • Checking the efficiency of proposed technique by travel length and travel time against standalone techniques. • Evaluation of proposed technique against previously developed technique cluttered environment. [ABSTRACT FROM AUTHOR]

This research work focuses on navigational strategy of humanoid robots in complex environments using a fuzzy embedded neural network based controller. The obstacle distances are measured from robot's current position and referred as front obstacle distance, right obstacle distance and left obstacle distance. These obstacle distances are served as input variables to the neural network model, and target angle is obtained as output parameter. The target angle obtained from neural network is fed to the Mamdani fuzzy system along with the obstacle distances as input variables to obtain the effective target angle for the humanoid robot. A Petri-net controller is embedded with developed neuro-fuzzy controller to perform dynamic path analysis in complex workspaces Single as well as multiple humanoid robots are used to analyze simulation and experimental navigation in different complex environments using developed neuro-fuzzy-petri-net controller. Various simulations are carried out using V-REP simulation software and similar scenario as per simulation is developed under laboratory conditions for various experimental navigation. The results from both the scenarios are related and are found to be in good covenant with each other having permissible range of errors. Simulation and experimental results in relation to navigational parameters shows the robustness of the developed controller. Surface plots and contour plots developed from the designed controller shows the effectiveness and efficacy in achieving global path during motion planning through optimizing target angle. To validate the results and to find out the effectiveness, the developed controller is compared with existing techniques such as IDQ and substantial progress of 16.66% in relation to path length is observed. • Designing the hybrid neuro-fuzzy controller. • Application in simulation platform. • Validation in experimental platform. • Comparison of results from simulation and experimental platforms. • Evaluation of the proposed controller against another existing controller. [ABSTRACT FROM AUTHOR]

Proper motion planning algorithms are necessary for both robotic manipulators and mobile robots (as well their combination, i.e. mobile manipulators) in order to execute their specific tasks. To solve this problem, current research work introduces the inverse kinematic models for mobile manipulators. In general a systematic closed form solution is not available in the case of inverse kinematic problem. So the solution for inverse kinematic problem is more complex as compared to direct kinematics problem. The current research work aims to combine the functionality of a robot arm with an autonomous platform. It means development of an autonomous wheeled mobile robot on which the robot arm is mounted. The purpose of this work is to integrate both the segments (i.e. mobile manipulator & mobile platform), such that the system can perform the constrained moves of the arm in the mean while as the platform is moving. [ABSTRACT FROM AUTHOR]

This paper analyses the effectiveness of a newly developed, multiple-crack diagnostic tool in dynamic structures using the multiple adaptive neuro-fuzzy inference system (MANFIS) technique. The effect of crack characteristics on the vibration responses have been investigated with different boundary conditions using finite element and numerical analysis. The first three relative natural frequencies, the difference of the first three average relative mode shapes, crack locations, and crack depths are used to train the fuzzy and neural controllers of the MANFIS system. The MANFIS controller is comprised of an input layer, a hidden layer and an output layer. The fuzzy segment uses the first three relative natural frequencies and the difference of the first three average relative mode shapes as the inputs. The hidden layer processes the outputs from the fuzzy controller. Finally, relative crack locations and relative crack depths are outputs from the developed MANFIS controller. It is observed that the predicted values of relative crack locations and relative crack depths from the formulated technique are well in agreement with the results from experimental analysis. The proposed methodology demonstrates its capability to be a suitable non destructive technique for fault identification in vibrating structures. [ABSTRACT FROM AUTHOR]

Premature failure of beam structure is observed due to presence of crack. In the current analysis a fuzzy inference system has been developed for detection of crack location and crack depth of a cracked cantilever beam structure. The six input parameters to the fuzzy member ship functions are percentage deviation of first three natural frequencies and first three mode shapes of the cantilever beam. The two output parameters of the fuzzy inference system are relative crack depth and relative crack location. Strain energy release rate at the crack section of the beam has been used for calculating its local stiffnesses. Different boundary conditions for the cracked beam structure are outlined during theoretical analysis for deriving the vibration signatures (mode shapes and natural frequencies). These signatures are subsequently used for deriving the fuzzy rules. Several fuzzy rules are derived and the Fuzzy inference system has been designed accordingly. Experimental setup has been developed for verifying the robustness of the developed fuzzy inference system. The developed fuzzy inference system can predict the location and depth of the crack in a close proximity to the real results. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic control technique is described for the prediction of structural damage. The input parameters to the fuzzy membership functions are relative to the deviation of the first three natural frequencies and relative to the values of percentage deviation for the first three mode shapes. The output parameters of the fuzzy inference system are relative crack depth and relative crack location. For deriving the fuzzy rules of natural frequencies, mode shapes, crack depths, and crack locations, theoretical expressions have been developed. Different boundary conditions are discussed, which take into account the relative crack location. Several fuzzy rules are derived, and the fuzzy controller has been designed accordingly. Experiments have been conducted for verifying the effectiveness of the developed fuzzy controller. The developed fuzzy controller is able to predict the location and depth of the crack, which are in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

• Design of hybrid controller for navigation of humanoid robot. • A decision making controller implemented for multiple humanoids navigation. • Simulation result and validation using real-time experiments confirms effectiveness. • Robustness is examined against the default controller based on torque produced. • Efficiency of controller is proved comparing against previously developed controller. 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. [ABSTRACT FROM AUTHOR]

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively. [ABSTRACT FROM AUTHOR]

The humanoid robot is widely used because of its ability to imitate human actions. The selection of navigational techniques is of prime importance because the quality of the opted technique directly affects the success of output. In this paper, the hybridization of the Dynamic Window Approach (DWA) and the Teaching–Learning-Based Optimization (TLBO) technique and its implementation on the NAO humanoid robot for navigation have been presented. The input is based on the location of obstacles and the target. The parameters are provided to the DWA technique, which decides the optimum velocity. The intermediate result is feed to the TLBO technique, which operates based on the teacher phase and the learner phase. This hybridization provides an optimum angle to take a turn and avoids the obstacles while moving towards the target. The current article concentrates on implementing hybridized techniques in static and dynamic terrains. Single NAO and some random obstacles are chosen for static navigation. For dynamic terrains, multiple NAOs and some static obstacles are considered. In this case, one humanoid robot acts as a dynamic obstacle to another. In the dynamic terrain, there is a possibility of inter-collision amongst NAOs. To avoid inter-collision, a Petri-Net controller has been designed and implemented in all NAOs. Simulation and experimental results on humanoid NAOs demonstrate target attainment with collision-free optimal paths. Experimental and simulated results of the proposed technique present an acceptable relation under 5 % and 6 % for a single robot and multiple robots, respectively. The proposed technique has been compared with previously developed techniques in complex, danger and dynamic terrains. In comparison with previously developed techniques, it is evident that the proposed technique is robust and efficient for the path planning of humanoid robots. • Navigation in static and dynamic terrain. • Implementation of hybrid DWA–TLBO​ technique for obstacle avoidance in humanoid NAO. • Implementation of Petri-Net controller for avoiding inter-collision. • Simulation and experiment on NAO humanoid robot in WEBOT and laboratory conditions respectively. • Comparison between hybrid DWA–TLBO technique with previously implemented technique. [ABSTRACT FROM AUTHOR]

In this paper, an artificial potential field based navigational controller has been developed for motion planning of humanoid robots. Here, NAO robots are used as the humanoid platform using the underlying principles of potential field based method. The movement of the robot is considered to be under a negative gradient scheme by the combined effect of attractive and repulsive forces generated due to target and obstacles, respectively. The working of the controller is tested in a V‐REP simulation platform, and the simulation results are validated through a real‐time experimental set‐up developed under laboratory conditions. Here, the navigation of both single and multiple humanoids has been attempted. For avoiding intercollision among multiple humanoids during their navigation in a common platform, a Petri‐Net control scheme has been proposed. The results obtained from both the simulation and experimental platforms are compared against each other with a good agreement between them having minimal percentage of deviations. Finally, the proposed controller is also evaluated against another existing navigational model, and a significant performance improvement has been observed. [ABSTRACT FROM AUTHOR]

The development of robotics research toward industrialization has created an enhanced demand for modernization of the automation industry. Humanoid robots being advanced than other forms of robots bear a large resemblance to humans and are very much helpful in replacing humans in tedious and repetitive tasks. Therefore, the navigation and path planning of the humanoids bear a large importance in robotics research. The current investigation deals with the path planning of NAO humanoid robots. In the present work, a classical method of regression analysis and an artificial intelligence technique of fuzzy logic are implemented separately for the purpose of obstacle avoidance during the motion of humanoid NAOs toward respective targets. The simulation analysis of the proposed techniques is carried out using V‐REP software. The experiments are performed in laboratory conditions with a proper environment for working of the humanoid NAOs. Finally, a comparison has been made between the simulation and experimental results. The results obtained from the simulation and experimental analyses are in good agreement with each other, which suggest that the proposed methodologies can be used as methods of robust control for the navigation of humanoids. The current investigation deals with the navigational analysis of humanoids using regression and fuzzy logic as the intelligent algorithms. Here, the proposed methodologies have been applied on both single as well as multiple humanoids on simulation and experimental platforms. Finally, the results obtained from both the platforms are compared against each other with good agreement. [ABSTRACT FROM AUTHOR]

In this study, two navigational controllers have been developed for the path planning of single as well as multiple humanoid robots in a cluttered environment using classical and computational intelligence approaches. Regression analysis and genetic algorithm have been used to design the proposed controllers. The regression controller is developed based on the left, right and front obstacle distances referenced from the humanoid’s current position and orientation and aims to calculate an optimized turning angle for minimum path length. The genetic algorithm controller is developed based on the nearest obstacle distance and goal position relative to the current position and orientation of the humanoid and aims to calculate its next best position for an optimized path length. To avoid inter-collision in the navigation of multiple humanoids, a Petri-Net controller has been implemented. The proposed algorithms have been successfully validated through multiple simulations in V-REP software. To test the effectiveness of the controllers, real-time experiments have also been conducted with NAO humanoids, and the results were compared with those obtained from the simulations. It was found that the experimental results closely resemble the simulations in terms of trajectories followed, path length covered and overall time taken with an acceptable error limit. The proposed navigational technique has also been compared with other existing navigational approaches to validate its effectiveness. Finally, it was concluded that the proposed navigational controllers are efficient in the path planning and obstacle avoidance and can be implemented on humanoid navigation in complex environments. [ABSTRACT FROM AUTHOR]

• Stabilization and path planning of humanoid robot is performed. • OTA, Surface plot, quiver and contour plot is generated by FLC. • Torque control is done by proportional-Integral-Derivative controller. • Roll and pitch angle fluctuate between less than ±1.5 degree. • Comparison with existing methodology has been carried out. In this study, path planning and stabilization of humanoids are carried out in an uneven path and dynamic environment. The importance of the work focuses on avoiding local minima and trapping in dead-ends during navigation. The sole purposes of this research are to i) Stabilize the humanoid on an uneven surface. ii) Develop proper path planning for the humanoid so that it can adequately navigate through obstacle-rich terrain. For achieving the above said objectives, the robot's controller is designed by tuning the Proportional-integral-derivative (PID) controller with a fuzzy logic controller (FLC) for better performance. The PID controller does joint angle control and torque control respectively. In contrast, a complicated task like generating optimized turning angle (OTA) and adjusting feet angle during stepping upon an uneven path is done by FLC. Gait generation during stepping on and off an uneven surface for the humanoid robot is discussed and implementation of the inverted pendulum plus flywheel method (LIPPFM) is used for the analysis of the dynamics of motion and removing the height constraint (center of mass) where the upper body is considered as the mass of the pendulum. Petri net controller is used to navigate humanoids in an environment with multiple humanoids. To examine the proposed controller's performance, the controller undergoes testing in simulation and experimental set up, and the obtained results are compared with recently developed techniques. V-REP software is used for conducting the simulation with an arena-size of 240*160 dimensions to test the effectiveness of the developed controller. A less than 5 % deviation is found because of friction and signal delay is noticed between simulation and the experimental result obtained for navigation while comparing to the previously developed technique such as PEM. There is a significant improvement in path length by 10.49 % is noticed and a decrease of 2.98 % in computational time is noticed. When the navigation parameter of the FUZZY-PID controller is compared with Genetic Potential Field (GPF), Pseudo Bacterial Potential Field (PBPF), and Bacterial Potential Field (BPF), there is an improvement of 15.41 %, 12.51 %, and 8.56 % respectively are noticed. It has been seen that the FUZZY-PID controller minimizes the settling time and lower the peak overshoot. When the humanoid robot crosses the uneven path using the proposed FUZZY-PID controller, the graph obtained is smoother than the previously developed technique. It displays the body attitude angle falling between less than ±1.5 degrees compared to ±2 degrees by the previously developed method which justify the selection of the FUZZY-PID controller. [ABSTRACT FROM AUTHOR]

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

Humanoid robots hold a decent advantage over wheeled robots because of their ability to mimic human exile. The presented paper proposes a novel strategy for trajectory planning in a cluttered terrain using the hybridized controller modeled on the basis of modified MANFIS (multiple adaptive neuro-fuzzy inference system) and MOSFO (multi-objective sunflower optimization) techniques. The controller works in a two-step mechanism. The input parameters, i.e., obstacle distances and target direction, are first fed to the MANFIS controller, which generates a steering angle in both directions of an obstacle to dodge it. The intermediate steering angles are obtained based on the training model. The final steering angle to avoid obstacles is selected based on the direction of the target and additional obstacles in the path. It is further works as input for the MOSFO technique, which provides the ultimate steering angle. Using the proposed technique, various simulations are carried out in the WEBOT simulator, which shows a deviation under 5% when the results are validated in real-time experiments, revealing the technique to be robust. To resolve the complication of providing preference to the robot during deadlock condition in multi-humanoids system, the dining philosopher controller is implemented. The efficiency of the proposed technique is examined through the comparisons with the default controller of NAO based on toques produces at various joints that present an average improvement of 6.12%, 7.05% and 15.04% in ankle, knee and hip, respectively. It is further compared against the existed navigational strategy in multiple robot systems that also displays an acceptable improvement in travel length. In comparison in reference to the existing controller, the proposed technique emerges to be a clear winner by portraying its superiority., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

Gait planning for the humanoid robot is a very essential and basic requirement. The humanoid robot is balanced at two feet; therefore, special attention is required for gait analysis for the execution of assigned tasks. In this paper, the linear inverted pendulum (LIPM) model is considered to simplify the study and to obtain better gait planning of humanoid robot NAO. Center of mass (COM) and zero moment point (ZMP) criterion are applied with the LIPM model for a better understanding of selecting the step length and period. In addition, a PSO (particle swarm optimization) tuned PID (proportional-integral-derivative) controller has been implemented. Sensory data such as the location of obstacles and the target along with the desired trajectory aided inverse kinematics have been embedded to the conventional PID controller, which provides an interim angle to start the navigation. This interim angle has been carried forward to the PSO technique accompanied by the desired trajectory. It tunes the parameters of the conventional PID controller and provides an optimum turning angle, which avoids obstacles and increases the stabilization of the robot while crossing it. It reduces travel time and shortens travel length. PSO technique minimizes the computational complexity and number of iteration because it requires fewer tuning parameters. Simulations are executed on the simulated NAO robot for the conventional PID controller and the proposed controller. To ratify its findings, experiments are carried out on a real NAO robot in laboratory conditions for both the conventional PID controller and the proposed controller. Simulation and experimental results are presenting a good agreement among each other with deviation under 6%. Applying the PSO tuned PID controller provides a predictable gait and reduces the stabilization time and essentially eliminating the overshoot by 25%. A comparative study with various controllers is performed, and the credibility of the evaluated result has been examined using statistical analysis. The proposed controller has been compared with a previously developed technique to ensure its robustness., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)