Search

In the current investigation, a two-stage hybridization model has been used for the motion planning of humanoids in complex environmental conditions using regression analysis and the firefly algorithm. In the first step, sensory outputs are fed to the regression model, and an initial turning angle (ITA) is obtained. In the second step, the ITA is again fed as input to the firefly model along with other required inputs, and the final turning angle (FTA) is obtained. The FTA serves as the guiding parameter for the humanoids to reach their desired destinations. The developed motion planning scheme has been implemented on NAO humanoid robots in simulation and experimental platforms. A Petri-Net control strategy has been integrated along with the hybrid scheme while negotiating multiple humanoids in a common platform. The results obtained from the motion planning analysis in simulation and experimental arenas are compared against each other in terms of selected navigational parameters and observed satisfactory agreements. Finally, the proposed hybrid controller is also tested against another standard navigational model and substantial enhancement in the performance has been noticed. [ABSTRACT FROM AUTHOR]

The humanoid robot garners paramount interest because of its ability to mimic human-like behavior in real-time environments. In this paper, a hybridized Artificial Potential Field (APF)-Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton technique is being proposed for the trajectory planning of the humanoid robot. The proposed methodology combines the faster local search BFGS Quasi-Newton method with the global search APF method for an efficient and faster-hybridized trajectory planning technique. The humanoid robot herein is being tested in a multi-humanoid working space having uneven surfaces. The data obtained from the sensors concerning the location of obstacles and the target are at first imparted to the APF method, which supplies an intermediate turning angle dependent on the predesigned training model of the APF method. The obtained turning angle is then fed into the BFGS quasi-newton method to produce an optimum turning angle, which inevitably guides the humanoid robots to the target by keeping a minimum safe distance from the obstacles. Multi-humanoid robots are employed in an environment having random static obstacles, with unique targets for them to reach. The proposal of using a multi-humanoid system arises the chance of inter-collision among the humanoids. To get rid of this situation, the dining philosophers controller is being implemented. The simulations and experiments are carried using the proposed technique to ratify the efficacy of the proposed technique. The experimental and simulation results yield a suitable acceptance rate under 5%. In comparison with the previously used navigational technique, it proves to be comfortably skillful and robust for the trajectory planning of the humanoid robot. [ABSTRACT FROM AUTHOR]

In the era of robotics, path planning and navigation of bipeds in multifaceted environments have always remained as one of the most promising areas of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both statistical technique and computational intelligence method for path planning of bipeds. The proposed path controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of bipeds in a cluttered environment. The proposed navigational controller is experimented for a bipedal robot in simulation and experimental environments. The outputs from various simulations and experimental results are compared and good agreement between navigational parameters like path length and path time is observed. Again a better efficacy has been observed by comparing the designed hybridization technique with an existing approach which validates the novel approach. [ABSTRACT FROM AUTHOR]

Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot's trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length. [ABSTRACT FROM AUTHOR]

SUMMARY: In the current research, kinematic analysis of a humanoid NAO is attempted. Here, both Denavit–Hartenberg (DH) parameter approach and multibody formulation approach have been analyzed. In the DH parameter approach, the NAO robot is solved by separating it into five individual kinematic chains. In the multibody formulation approach, NAO is divided into 15 segments, and each segment is analyzed. Kinematic analysis holds a significant importance; as from the data obtained in the kinematic analysis, the robots can be designed for real-time path planning and navigation. The current analysis is a novel approach to analyze the NAO based on its kinematic constraints. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions. [ABSTRACT FROM AUTHOR]

The primary conventional method for simplifying legged robots' complex walking dynamics involves using low-dimensional models such as the linear inverted pendulum model (LIPM). This paper emphasizes utilizing the LIPM plus flywheel model (LIPPFM) for analysis of the complete dynamic motion of the humanoid robot. Inclining toward a more realistic case, the model is improvised to remove the COM's height constraint (center of mass) and consider the effect of the upper body part using the mass of the pendulum. Furthermore, the double support phase is being discussed in the locomotion phase of the humanoid robot. MPC (model predictive control) approach has been used in this paper, which is tuned with the ACO (ant colony optimization) technique. The desired trajectory, joint angles, has been imparted to the MPC, which provides the robot's joint motion. This joint motion has been further transferred to ACO, optimizing the step adjustment and providing an expected trajectory to walk over an uneven surface. The simulation has been carried out in an uneven environment based on ACO tuned MPC controller, and further, it has been validated using real-time experiments on humanoid robot NAO. The controller shows a reasonable degree of efficiency in both the real NAO and simulated NAO with a deviation under 5%. The comparative study among various controller shows that the proposed controller lowers the peak overshoot and the settling time. In comparison with the previously developed controller, the deviation in roll angle and pitch angle justifies the selection of the proposed controller. [ABSTRACT FROM AUTHOR]

Prim's algorithm has demonstrated a very effective and selective method of solving the minimum spanning tree optimization problems. It is a greedy algorithm that starts from an empty spanning tree and reaches its goal by picking the minimum weight edges which alternately optimizes the path in less possible time. In this paper, the capability of prim's algorithm in designing the behavioural controller of a humanoid robot has been shown. Here, a new hybrid PA–Fuzzy motion planning approach has been proposed that uses the concept of minimizing the distance between the robot and obstacles as well as robot and target. An optimal turning angle is generated by the hybrid controller that helps to avoid the obstacles present in the arena to create a collision-free path. The results obtained from hybrid PA–Fuzzy motion planning procedure show the capability of the controller in achieving the optimal paths in different environments with both static and dynamic obstacles. The results observed from simulation and experimental arenas are found to be in satisfactory agreement with each other producing minimal error limits. The developed hybrid technique is compared with some existing methodologies, and significant improvement is found in relation to path length and computational time. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits. [ABSTRACT FROM AUTHOR]

SUMMARY: Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time. [ABSTRACT FROM AUTHOR]

SUMMARY: In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed. [ABSTRACT FROM AUTHOR]

Summary: The present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot's probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed. [ABSTRACT FROM AUTHOR]

Summary: With an ability to mimic the human behaviour and replace human efforts in proper platforms, humanoid robots have always acquired a special place among robotics practitioners. Being a complex method of analysis, navigation and path planning, humanoid robots still possess an interesting yet challenging area of investigation. In the current work, a novel navigational strategy has been proposed for smooth and hassle-free movement of single as well as multi-humanoid robots in complex environments. Here, the navigational plan is based on a virtual target displacement strategy which is activated when the robot is unable to find a safe path along the actual target line. After detection of a potential obstacle by the sensors of the robot, a number of virtual targets are generated around the actual target. Then, the most feasible path and point to move are calculated by assigning suitable weightage through several selected parameters to each target line and visualizing the safest path. The proposed approach is implemented on a V-REP simulation platform, and the simulation results are also validated against an experimental set-up prepared under test conditions. The validation of simulation results against experimental counterparts has revealed satisfactory agreement between them. To avoid possibility of any inter-collision during navigation of multi-humanoids under a common platform, a Petri-Net strategy has been integrated along with the proposed control strategy. Finally, the developed approach is also assessed against another existing navigational controller, and a significant performance improvement has been observed. [ABSTRACT FROM AUTHOR]

Cracks, faults, or damages are serious threat to the current and future performance of the system. For decades, research work is being carried out on the dynamic behavior of the structural elements for fault diagnosis. The modern types of damage detection methods use the dynamic response from the signals of the beams. In this article, a robust fault diagnostic tool based on clonal selection algorithm and fuzzy logic has been proposed. Theoretical and finite element analyses are done to model the crack and to find the effect of the presence of cracks changes on the vibrational characteristic (natural frequencies) of a fixed–fixed beam. The inputs to (Clonal Selection Algorithm-Fuzzy Logic) system are the first three relative natural frequencies and the outputs from the system are the relative crack depth and relative crack location. Experimental validations with the theoretical results have been carried out to check the robustness of the predicted algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of trajectory tracking and control of a low-cost flying agent, equipped with a microcontroller for real-time navigation. A flying agent (aircraft-type quadcopter) has been designed and developed for steering control along a planned trajectory. A novel control scheme is developed usingMATLAB/Simulink to ensure the right balance among pitch, roll and yawof the flying agent wings. The proposed control scheme utilises a PID controller which regulates the error dynamics to stabilise the navigation. Various trajectories have been generated and verified through simulation studies as well as tested in a real model. The real-time experiments verify the manoeuvrability of the controller, which also checks the compatibility with hardware interfaces. [ABSTRACT FROM AUTHOR]

This work is focused on determining the response of a multi-cracked structure in the presence of different types of cracks vibrated due to a transit mass. The open transverse and inclined edge cracks of random crack depth are present at various locations of the cracked structure. The mass is moving on the beam at the different critical speeds of the structure. Runge-Kutta fourth order method is employed to evaluate the response of the structure numerically. The significance of different factors like the magnitude of the moving mass, moving speed, crack depth, crack inclination angle and their effects on the response of the deteriorated structure are investigated. Numerical analyses with numerous examples are carried out and validated the results with finite element analysis (FEA) and experimental investigations. [ABSTRACT FROM AUTHOR]

This paper addresses the coordination and control of a wheeled mobile manipulator (WMM) using artificial immune system. The aim of the developed methodology is to navigate the system autonomously and transport jobs and tools in manufacturing environments. This study integrates the kinematic structures of a four-axis manipulator and a differential wheeled mobile platform. The motion of the developed WMM is controlled by the complete system of parametric equation in terms of joint velocities and makes the robot to follow desired trajectories by the manipulator and platform within its workspace. The developed robot system performs its action intelligently according to the sensed environmental criteria within its search space. To verify the effectiveness of the proposed immune-based motion planner for WMM, simulations as well as experimental results are presented in various unknown environments. [ABSTRACT FROM AUTHOR]

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller. [ABSTRACT FROM AUTHOR]

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]

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]

• 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]

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm. [ABSTRACT FROM AUTHOR]

With the development of science and technology, robots have become an integral part of human life. To ease human efforts in tedious and repetitive tasks, robots are used in human platforms. The current analysis is focused on the design and development of a robotic manipulator to perform pick and place like operations. First, the kinematic analysis is performed on different links of the designed manipulator to know its workspace and singularity. Each link of the manipulator is modelled in SOLIDWORKS software and simultaneously imported to V-REP software. In the V-REP software, links are grouped to form the manipulator arm. Each link is attached to a joint. After formation of the manipulator arm, simulations for different physical operations are performed. While performing different physical operations with the manipulator arm, graphical analysis is performed for position, velocity and acceleration of revolute joints. The force required for the prismatic joint to hold the object is also calculated. The results obtained from the simulation analysis reveal that the manipulator can work satisfactorily in the practical environment. Finally, the results are compared for manipulators of different degrees of freedom to obtain the workspace required for a smooth operation. The current study holds a large importance in robotics field as mobile manipulators are extensively used in different industries and the same analysis can also be extended towards other forms robots. Abbreviation: DLS: Damped Least Square DH: Denavit-Hartenberg V-REP: Virtual Robot Experimentation Platform IK: Inverse Kinematics [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]

The current investigation is focused on the development of a novel navigational controller for the optimized path planning and navigation of humanoid robots. The proposed navigational controller works on the principle of adaptive particle swarm optimization. To improve the working pattern of a simple particle swarm optimization controller, some modifications are done to the controlling parameters of the algorithm. The input parameters to the controller are the sensory information in forms of obstacle distances, and the output from the controller is the required turning angle to safely reach the target position by avoiding the obstacles present in the path. By applying the logic of the adaptive particle swarm optimization, humanoid robots are tested in simulation environments. To validate the results, an experimental platform is also developed under laboratory conditions, and a comparison has been performed between the simulation and experimental results. To test the proposed controller in both static and dynamic environments, it is implemented in the navigation of single as well as multiple humanoid robots. Finally, to ensure the efficacy of the proposed controller, it is compared with some of the existing techniques available for navigational purpose. [ABSTRACT FROM AUTHOR]

In recent years, significant developments have been noticed in nondestructive techniques for damage detection in cracked structures. Some of the proposed methods can be used to find out the existence of the crack; other methods locate and simultaneously find out the damage severity. In the current investigation, a novel hybridised method is proposed for damage detection in structural elements. The proposed method can be used to investigate both location and nature of damage in structures within a reasonable time limit. The problem in the current analysis requires a set of dynamic parameters that depend on the dynamics of the cracked structure due to the presence of the crack. In the present study, the first three natural frequencies of a structure are considered as the inputs to find out the damage location. A finite element method is used to generate the first three natural frequencies of a cracked cantilever beam with multiple cracks. A method hybridizing the nature-inspired artificial intelligence techniques has been implemented for crack detection. Here, clonal selection algorithm and genetic algorithm have been integrated to design the framework of the hybrid technique. The changes in the natural frequencies are given as inputs to the hybrid technique and the output from the technique is the locations of damage. [ABSTRACT FROM AUTHOR]

This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path‐planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot‐obstacle and robot‐goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real‐world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms. [ABSTRACT FROM AUTHOR]

Detection of the cracks in beam structures using the fuzzy Gaussian inference technique has been investigated in this paper. The fuzzy-logic controller used in the present investigation consists of six input parameters and two output parameters. The input parameters to the fuzzy controller are the relative divergence of the first three natural frequencies and first three mode shapes in dimensionless forms. The output parameters of the fuzzy controller are the relative crack depth and relative crack location in dimensionless forms. To derive the fuzzy rules for the controller, theoretical expressions have been developed considering four parameters such as natural frequencies, mode shapes, crack depths, and crack locations. The strain-energy release rate has been used for calculating the local stiffnesses of the beam for a mode-I type of the crack. Several boundary conditions are outlined that take into account the crack location. Required fuzzy rules are derived for the fuzzy Gaussian controller. The experimental setup has been fabricated in the laboratory for verifying the robustness of the developed fuzzy controller. The developed fuzzy controller can predict the location and depth of the crack in close proximity with the real results. [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]

This paper addresses the performance comparison of Radial Basis Function Neural Network (RBFNN) with novel Wavelet Neural Network (WNN) of designing intelligent controllers for path planning of mobile robot in an unknown environment. In the proposed WNN, different types of activation functions such as Mexican Hat, Gaussian and Morlet wavelet functions are used in the hidden nodes. The neural networks are trained by an intelligent supervised learning technique so that the robot makes a collision-free path in the unknown environment during navigation from different starting points to targets/goals. The efficiency of two algorithms is compared using some MATLAB simulations and experimental setup with Arduino Mega 2560 microcontroller in terms of path length and time taken to reach the target as an indicator for the accuracy of the network models. [ABSTRACT FROM AUTHOR]

This paper presents a damage detection technique combining analytical and experimental investigations on a cantilever aluminium alloy beam with a transverse surface crack. Firstly, the first three natural frequencies were determined using analytical methods based on strain energy release rate. Secondly, an experimental method was adopted to validate the theoretical findings. The damage location and severity assessment is the third stage and is formulated as a constrained optimisation problem and solved using the proposed differential evolution (DE) algorithm based on the measured and calculated first three natural frequencies as inputs. Numerical simulation studies indicate that the proposed method is robust and can be used effectively in structural health monitoring (SHM) applications. [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]