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This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments.

The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point.

Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these.

Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration.

Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques.

In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement.

The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.

This paper aims to generate an obstacle free real time optimal path in a cluttered environment for a two-wheeled mobile robot (TWMR).

This TWMR resembles an inverted pendulum having an intermediate body mounted on a robotic mobile platform with two wheels driven by two DC motors separately. In this article, a novel motion planning strategy named as DAYANI arc contour intelligent technique has been proposed for navigation of the two-wheeled self-balancing robot in a global environment populated by obstacles. The developed new path planning algorithm evaluates the best next feasible point of motion considering five weight functions from an arc contour depending upon five separate navigational parameters.

Authenticity of the proposed navigational algorithm has been demonstrated by computing the path length and time taken through a series of simulations and experimental verifications and the average percentage of error is found to be about 6%.

This robot dynamically stabilizes itself with taller configuration, can spin on the spot and rove along through obstacles with smaller footprints. This diversifies its areas of application to both indoor and outdoor environments especially with very narrow spaces, sharp turns and inclined surfaces where its multi-wheel counterparts feel difficult to perform.

A new obstacle avoidance and path planning algorithm through incremental step advancement by evaluating the best next feasible point of motion has been established and verified through both experiment and simulation.

This paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment.

This controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values.

The path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments.

A hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.

This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.

The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.

The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.

As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.

This paper aims to propose an optimized overview of firefly algorithm (FA) over physical-natural impression of fireflies and its application in mobile robot navigation under the natural intelligence mechanism.

The brightness and luminosity are the decision variables in proposed study. The paper achieves the two major goals of robot navigation; first, the optimum path generation and, second, as an obstacle avoidance by co-in-centric sphere-based geometrical technique. This technique comprises the optimum path decision to objective function and constraints to paths and obstacles as the function of algebraic-geometry co-relation. Co-in-centric sphere is the proposed technique to correlate the constraints.

It is found that the present FA based on concentric sphere is suitable for efficient navigation of mobile robots at the level of optimum significance when compared with other approaches.

The paper introduces a novel approach to implement the FA for unknown and uncertain environment.

The present paper deals with the finite element dynamic analysis of delaminated composite twisted plates based on a simple multiple delaminated model. Eight‐noded isoparametric quadratic elements are used to develop the finite element analysis procedure. Composite plates are assumed to contain both single and multiple delaminations. To investigate the dynamic behaviour of delaminated twisted plates, numerical results for free vibration, forced vibration and impact response are generated by varying the size and location of delaminations as well as the stacking sequence and other geometrical parameters.

This paper aims to design a Takagi–Sugeno fuzzy model with a simulated annealing hybrid algorithm (fuzzy-SAA) that was implemented for mobile robot navigation and obstacle avoidance in a cluttered environment.

The SAA is used to optimize the output parameters of the fuzzy controller. The ultrasonic range finder sensor and sharp infrared range sensor are used for calculating the different obstacle distances, such as front, right and left obstacle distance, for selecting the suitable steering angle control command in the environment.

The simulation and experimental results show the proposed method is feasible and valid for a wheeled mobile robot moving in a cluttered environment.

The developed fuzzy-SAA hybrid algorithm provides better results (in terms of navigation path length and time) as compared to previous works, which verifies the effectiveness and efficiency of the developed hybrid algorithm.

With the widespread use of the composites over other metallic materials in different fields of engineering, studies on damages of composite structures have assumed great importance. Among various kinds of damages, delamination is of very serious concern to composite applications. It may arise as a consequence of impact loading, stress concentration near a geometrical or material discontinuity or manufacturing defects. The presence of one or more delaminations in the composite laminate may lead to a premature collapse of the structure due to buckling at a lower level of compressive loading. So the effect of delamination on stability of composite structures needs attention and thus constitutes a problem of current interest. The purpose of this paper is to deal with both numerical and experimental investigations on buckling behaviour of single and multiple, delaminated, industry driven, woven roving glass/epoxy composite plates on clamped free clamped free (CFCF) rectangular plates.

For numerical analysis, a finite element model was developed with an eight noded two dimensional quadratic isoparametric element having five degrees of freedom per node. The elastic stiffness matrices were derived using linear first order shear deformation theory with a shear correction factor. Green's nonlinear strain equations are used to derive the geometric stiffness matrix. The computation of buckling load based on present formulation is compared with the experimental results for the effect of different parameters on critical load of the delaminated composite panels. In the experimental study, the influences of various parameters such as delamination area, fiber orientations, number of layers, aspect ratios on the buckling behaviour of single and multiple delaminated woven roving glass/epoxy composite plates were investigated. Buckling loads were measured by INSTRON 1195 machine for the delaminated composite plates.

Comparison of numerical results with experimental results showed a good agreement. Both the results revealed that the area of delaminations, fiber orientations, number of layers and aspect ratio have paramount influence on the buckling behaviour of delaminated plate.

The present study is part of Jayaram Mohanty's doctoral thesis, an original research work.

The purpose of this paper is to study the nonlinear forced vibration responses of delaminated composite shells in hygrothermal environments.

Finite element method using an eight-noded C0 continuity, isoparametric quadrilateral element is employed. The theoretical formulations are based on the first order shear deformation theory and von Kármán type nonlinear kinematics. For modeling the delamination, multipoint constraint algorithm is incorporated in the finite element code.

The effect of delaminations on the nonlinear transient response of delaminated composite shells is dependent not only on the size but also on the location of the delaminations and hygrothermal environments.

The present study is limited to cylindrical and spherical shells having rectangular planform containing single delamination. Studies on different shell forms having non rectangular planforms containing multiple delaminations can be taken up for future research.

Nonlinear transient response of delaminated shells in hygrothermal environments is studied for the first time. It will assist researchers of nonlinear dynamic behavior of elastic systems.