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The increasing demand for automation and material transportation has shown an incline toward optimal path navigation. The present work implements an intelligent Memory-based gravitational search algorithm (MGSA) with an evolutionary learning strategy to achieve a globally optimal collision-free path. The Evolutionary learning strategy helps improve the diversity among the Gravitational masses/agents, hence improving the overall exploration capability of the model. While the other approaches focus more on an evolutionary strategy based on mutation and cross-overs, the present technique implements the evolutionary strategy based on the position of the fit agents to improve the position of the unfit agents in the population. It ensures a fast-converging path planning result with an improved trajectory. Further, adding a memory-based approach helps the model remember the location of the best agent within the population. The controller is tested with multiple Humanoids on even and uneven terrains and showed a minimal improvement of more than 4% in path length with a minimum 5% deviation in the simulation and experimental results. The proposed approach showed a further improvement of more than 6% compared to the different intelligent path-planning approaches in a similar environment.

Purpose: Smooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal. Design/methodology/approach: Therefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot. Findings: During experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables. Originality/value: After literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Fuzzy logic is widely known as a value-based technique. Whale optimization algorithm (WOA), on the other hand, is a nature-inspired optimization technique. Hybridization of these two techniques is proposed for path planning and control, over multiple mobile robots in static and dynamic environments. The effectiveness of the resulting technique, known as ‘Fuzzy-WOA’, is tested through MATLAB simulation coupled with real-time experiments. Upon testing, a good agreement is observed between these platforms. Furthermore, the proposed technique is found to be more efficient when compared to other existing techniques, with a significant improvement of about 20.63% in terms of path lengths.

ABSTRACTWith 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

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

Purpose: Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel). Design/methodology/approach: The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot. Findings: The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller. Originality/value: Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. Virtual Robot Experimentation Platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.Graphical Abstract

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

Purpose: In the present investigation, vibration signatures have been used for identifying the place of crack and depth in pre-cracked FRP beams. Bidirectional (woven) FRP composite beams are consisting of 13 layers of epoxy–glass fibres (0°/7.5°/15°/22.5°/30°/37.5°/45°). Effects of clamped–free and clamped–clamped end conditions have been studied in the current investigation. Methods: Fibre orientations effect on dynamics of FRP beam by the altering transverse crack location and its depth have been observed by applying analytical, finite element method, and neural network techniques. Results: The results obtained have been verified experimentally. The outcomes of both methods have deviation within 6% during comparison. Conclusion: From the investigation, it has been concluded that the natural frequencies and mode shapes can be used for identifying crack location and crack depth for different fibre orientation in FRP beam.

Purpose: Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach: Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings: By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value: Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

With the increasing use of humanoid robots in several sectors of industrial automation and manufacturing, navigation and path planning of humanoids has emerged as one of the most promising area of research. In this paper, a navigational controller has been developed for a humanoid by using fuzzy logic as an intelligent algorithm for avoiding the obstacles present in the environment and reach the desired target position safely. Here, the controller has been designed by careful consideration of the navigational parameters by the help of fuzzy rules. The sensory information regarding obstacle distances and bearing angle towards the target are considered as inputs to the controller and necessary velocities for avoiding the obstacles are obtained as outputs. The working of the controller has been tested on a NAO humanoid robot in V-REP simulation platform. To validate the simulation results, an experimental platform has been designed under laboratory conditions, and experimental analysis has been performed. Finally, the results obtained from both the environments are compared against each other with a good agreement between them.

This article introduces a singleton type-1 fuzzy logic system (T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.

This article focuses on the navigational control of underwater mobile robot. Differential evolution approach has been used to navigate the underwater robot from source to destination while avoiding various types of obstacles. Differential evolution algorithm has been employed to find out the robot’s global best pose among a set of possible solutions based on the fitness value with respect to the current sensory data about obstacles and target. Such evolutionary computation scheme can provide desirable convergence, diversity and also robustness depending on proper selection and adaptive tuning of parameters. Self-learning ability of the parameters in the path planning algorithm is crucial to deal with nonlinearities and ambiguities of hydrodynamics as created by high-frequency oscillations during underwater motion. A sequence of intermediate positions chosen by proposed dynamic differential evolution algorithm between start and goal points can be defined as a near-optimum path for underwater robot. During navigation of the robot, the path smoothness and clearance from obstacles and computational time are also considered for performance evaluation of implemented algorithm. The feasibility of the proposed underwater motion planning approach has been authenticated through the simulation and experimental results.

This research article presents a computational work followed by experiments to elaborate the response of a multi-cracked beam structures excited by traversed mass. The cracks are located at different positions having different relative crack depths. A fourth-order Runge–Kutta computational method is used here for finding the deflection of the cracked beam. The responses of damaged cantilever beam and fixed-fixed beam are considered in this work. During the traversing of the mass across the damaged structure, the response of the cracked structures has been analysed. The effects of mass, speed, crack severities and crack locations on the response of the structure have also been inspected. Computational work has been carried out with different masses at various speeds for the moving mass. The results obtained from the computational work have been validated with that of experiments and are found to be in good agreement.

The prediction and decision-making features of the artificial neural network have been applied to several robotic related research works. Humanoid robot navigation is a challenging task for the researchers with best decision making. Simple artificial neural network (ANN) is the connection models which are poor in multistep decision making with long term planning and logical reasoning. So, in this study artificial neural network is improved in long term cognitive planning by SOAR (State Operator and Result) with powerful feature prediction through ANN (SANN) and hybridized with Fuzzy system for generation effective turning angle value, which will guide the humanoids to reach target location with better decision target seeking ability. An intelligent data fusion model is embedded between SOAR and ANN for converting the logical sequence information of SOAR to probabilistic vectors in multilayer ANNs. The obstacle distances are the input parameters to the SANN model and turning angle (TA) is the output from SANN. The obstacle distances along with the TA obtained from SANN is fed to the fuzzy system as input value and effective turning angle (ETA) is obtained to guide the humanoids towards reaching target. The proposed novel technique and ANN is used by both single and multiple humanoids in both simulation and experimental platforms. The novelty of the research focuses on the development of the new SOAR-ANN technique with better decision strategy for effective turning angle value, to obtain global optimal path by avoiding trapping in local optima and dead ends. The simulation and experimental results in terms of path length travelled and time spent to reach target are obtained from both ANN and novel SANN-Fuzzy technique. When the developed novel technique is compared with standalone ANN method during single humanoid navigation then, a significant improvement of 2–5% and 14–15% is observed in related to trajectory path span and trajectory time spent respectively. Similarly, during multiple humanoid navigation percentage improvement of 5–9% and 14–18% is obtained for humanoids in terms of navigational parameters respectively. The results clearly shows the developed technique requires less time and travels less path to reach target as compared to ANN and the percentage of error is also quite low. Statistical analysis is performed from the results of both the techniques and reflects the effectiveness of the proposed technique in comparison to ANN. Further the developed technique is compared against another existing methodologies like Fuzzy-ANN and Boundary Node Approach and, significant enhancements of 5.64% and 9.72% is attained in terms of track span respectively. • Implementation of SOARANN technique over ANN for better feature prediction and better decision making strategy. • Developed hybridized SANN-Fuzzy technique for generating effective turning angle. • An intelligent data fusion model is embedded between SOAR and ANN for converting the logical sequence information of SOAR to probabilistic vectors in multilayer ANNs. • Comparison of ANN with SANN-Fuzzy technique in simulation as well in experimental arena. • Comparison of developed SANN-Fuzzy with existing methodology is performed. [ABSTRACT FROM AUTHOR]

The present investigation is an attempt to evaluate the dynamic behaviours of multi-cracked cantilever rotor shaft with an additional mass attached at the tip of the shaft, which is partially submerged in the viscous fluid. In this work, theoretical expressions are developed to find the fundamental natural frequency and amplitude of the multi-cracked rotor shaft with attached mass, using influence coefficient method. Navier–Stoke’s equations are used for the analysis of external fluid forces acting on the rotor. Viscosities of the fluid and relative crack locations are taken as main variable parameters. For the analysis, suitable theoretical expressions are considered, and the Matlab programming is made to obtain the results. Experimental verifications are also performed to prove the validity of the theory developed.

Humanoid robots must be capable of walking on complicated terrains and tackling a variety of obstacles leading to their wide range of possible implementations. To that aim, in this article, the issue of humanoid robots walking on uneven terrain and tackling static and dynamic obstacles is examined. It is inspected by implementing a novel Enhanced DAYANI Arc Contour Intelligent (EDACI) Algorithm that designs trajectory by searching feasible points in the environment. It provides an optimum steering angle, and step optimization is performed by Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton method that leads to guide the humanoid robot stably to the target. The leg length policy has been presented, and a reward-based system has been implemented in the walking pattern generator that provides the optimum gait parameters. One humanoid robot act as a dynamic obstacle to others if they are navigating on a single terrain. It may generate a situation of deadlock, which needs to be solved. In this article, a dining philosopher controller (DPC) is employed to deal with and solve this issue. Simulations are used to evaluate the proposed approach in several uneven terrains having two humanoid NAOs. The findings indicate that it can precisely and efficiently produce optimal collision-free paths, demonstrating its efficacy. Experiments in similar terrain are performed that validate the results with a deviation under 6%. The energy efficiency of the developed controller is evaluated in reference to the inbuilt controller of NAO based on energy consumption. In order to check the feasibility and accuracy of the developed controller, a comparison with an established technique is provided.

Vibration-based inspection is an area of active research. This task is performed by estimating the effects of structural damage on the eigen parameters of structures. The problem of detecting, locating, and quantifying the extent of damage was under study for several decades. In order to investigate the prevailing effects of damage present in the structure under examination, a mathematical model of the damage must be introduced into the model of the structure at the location of the fault. While focusing on transverse vibrations, a simple stiffness reduction of the damaged region was used. In the present work the vibration parameters (relative first three natural frequencies) of the cracked structure are treated in a controller. The controller is designed using fuzzy logic & genetic algorithm. Here the fuzzy rules are optimized by using genetic algorithm. Using the controller, the crack locations can be determined.

In this paper, the crack identification using smart technique (by several hybrid membership functions in a fuzzy controller) has been developed for inverse analysis of the vibration signatures (like modal frequencies, mode shapes) and crack parameters (like crack depth, crack location and crack inclination) of an inclined edge crack cantilever beam. The modal frequencies are obtained from finite element (using ANSYS) and experimental analysis which are used as inputs to the hybrid fuzzy controller. The hybrid fuzzy system is designed by taking different types of membership functions (MF) to determine the crack parameters. The calculated first three modal frequencies are used to create number of fuzzy rules with the three output crack parameters. Finally, the proposed hybrid technique is validated by comparing the results obtained from trapezoidal and Gaussian fuzzy controllers, FEA and experimental results. The outcomes obtained from hybrid fuzzy controller are in good agreement with experimental results. Nomenclature

In this paper, a novel approach for detecting crack location and its intensity in cantilever beam by Fuzzy logic techniques is established. The analysis has been done by using ANSYS FE software. The fuzzy controller with Bell shaped membership functions are used here which consists of three input parameters are relative deviation of first three natural frequencies and two output parameters are relative crack depth and relative crack location respectively. A series of fuzzy rules are resulting from vibration parameters which are finally used for prediction of crack location and its intensity. This method provides the knowledge towards the detection, location and characterization of the damage in the cantilever beam.

This paper is related to the human perception based idea by using heuristic information for the navigation of mobile robots in cluttered dynamic environments which provides a general, robust, safe and optimized path. The heuristic rule base network consists of a simple algorithm which makes predefined estimation function very smaller. The estimation function should be adequately defined for desired movement in the environments. A navigation system using rule based technique that allows a mobile robot to travel in an environment about, which the robot has no prior knowledge. This heuristic rule is applied in conjunction with artificial neural network. The proposed intelligent controller provides an optimum trajectory which increases the effectiveness of a mobile robot. A series of simulations test has been conducted to show the effectiveness of the proposed algorithm.

In this article, a soft-computing-fuzzy-logic approach for crack identification in cantilever beam has been considered. The fuzzy controller consists of six input parameters and two output parameters. The input parameters are first three relative natural frequencies and first three relative mode shape differences in dimensionless forms. The output parameters are relative crack location and relative crack depth. Theoretical analyses have been done including the effects of crack depths and crack locations on the natural frequencies and mode shapes. Several fuzzy rules are outlined for the fuzzy controller. Gaussian membership functions are used for the fuzzy controller. The local stiffnesses at crack location of the beam have been calculated using strain energy release rate. The fuzzy rules are used to identify the location and depth of the crack. Finally, the effectiveness of the developed fuzzy controller has been verified by the results obtained from the developed experimental set-up.

Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length.

Present research and development in the area of mobile robots mainly aims at study of various techniques, methods and sensors being used for navigation of mobile robots. Different techniques have been discussed for the navigation of mobile robots in the first part. These techniques can be subdivided as (1) fuzzy logic technique, (2) neural network technique and (3) genetic algorithm technique. In the second part, five methods are being discussed for navigation of mobile robots. These methods are (1) potential field method, (2) grid-type method, (3) heuristic method, (4) adaptive navigation method and (5) Virtual Impedance method. The last segment focuses on different sensors being used for navigation of mobile robots. The sensors discussed are (1) ultrasonic sensor, (2) laser sensor, (3) magnetic compass disk sensor, (4) infrared sensor and (5) vision (camera) sensor. Keeping the above strategies in forefront, a comprehensive discussion has been made and is described methodologically in the current paper.

Abstract This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.

This paper describes the use of a hybrid system comprising a multi-layer perceptron and a Petri Net model to control the navigation of multiple mobile robots in an unknown and cluttered environment. The multi-layer perceptron is trained with examples representing the typical static obstacles to be encountered by the robots and the evasive actions to be taken. The Petri Net model embodies the rules describing how the robots should move in order to avoid colliding against one another. The hybrid system has been implemented in simulation software as well as in actual mobile robots. The paper presents the results of simulation and practical tests that demonstrate the ability of groups of robots incorporating the proposed navigation system to negotiate various types of obstacles and find targets successfully.

Humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target; when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique.