Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques

Improved Modified Chaotic Invasive Weed Optimization Approach to Solve Multi-Target Assignment for Humanoid Robot

The paper presents an improved modified chaotic invasive weed optimization (IMCIWO) approach for solving a multi-target assignment for humanoid robot navigation. MCIWO is improved by utilizing the Bezier curve for smoothing the path and replaces the conventional split lines. In order to efficiently determine subsequent locations of the robot from the present location on the provided terrain, such that the routes to be specifically generated for the robot are relatively small, with the shortest distance from the barriers that have been generated using the IMCIWO approach. The MCIWO approach designed the path based on obstacles and targets position which is further smoothened by the Bezier curve. Simulations are performed which is further validated by real-time experiments in WEBOT and NAO robot respectively. They show good effectiveness with each other with a deviation of under 5%. Ultimately, the superiority of the developed approach is examined with existing techniques for navigation, and findings are substantially improved

E-puck motion control using multi-objective particle swarm optimization

This article describes the velocity-based motion and orientation control method for a differential-driven two-wheeled E-puck Robot (DDER) using the Multi-Objective Particle Swarm Optimization (MPSO) algorithm in the Virtual Robot Experimentation Platform (V-REP) software environment. The wheel velocities data and Infra-Red (IR) sensors reading make the multi-objective fitness functions for MPSO. We use front, left, and right IR sensors reading and right wheel velocity data to design the first fitness function for MPSO. Similarly, the front, left, and right IR sensors reading, and left wheel velocity data have been taken for making the second fitness function for MPSO. The multi-objective fitness functions of MPSO minimize the motion and orientation of the DDER during navigation. Due to the minimization of motion and orientation, the DDER covers less distance to reach the goal and takes less time. The Two-Dimensional (2D) and Three-Dimensional (3D) navigation results of the DDER among the scattered obstacles have been presented in the V-REP software environment. The comparative analysis with previously developed Invasive Weed Optimization (IWO) algorithm has also been performed to show the effectiveness and efficiency of the proposed MPSO algorithm

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems

Occupational exposure and awareness of Occupational safety and health among cloth dyeing workers in Jaipur India

Abstract: Objectives: We assessed the health risk factors and awareness of Occupational safety and health of workers in cloth dyeing industry of Jaipur. Methods: A pretested questionnaire was used to evaluate the health problems and awareness of occupational safety and health among workers. Results: The majority of these workers were suffering from eye irritation, back pain, allergies, general weakness, with most workers having three to five of these health problems. Our study reported higher incidence of musculoskeletal and respiratory diseases among workers in different age groups. Conclusion: A large number of diseases in different age groups is an indication that this industry exposes workers to many health hazards and lack of awareness and non availability of PPE in this industry is aggravating the health problems of the workers

Modeling of finishing force and torque in ultrasonic-assisted magnetic abrasive finishing process

A new finishing technique called ultrasonic-assisted magnetic abrasive finishing integrates ultrasonic vibration with magnetic abrasive finishing process for finishing of workpiece surface more efficiently as compared to magnetic abrasive finishing in the nanometer range. During finishing, two types of forces are generated in ultrasonic-assisted magnetic abrasive finishing, namely, a normal force (indentation force) and a tangential force (cutting force) that produces a torque. The finishing forces have direct control on the rate of change of surface roughness and material removal rate of the workpiece surface. This article deals with the theoretical modeling of the normal force and the finishing torque based on the process physics. In this work, finite element simulations of the electromagnet were performed to calculate a magnetic flux density in the working zone; they were also used to evaluate the normal force on the workpiece surface. The theory of friction for the abrasion of metals was applied together with the effect of ultrasonic vibration to calculate the finishing torque. The developed model predicts the normal force and finishing torque in ultrasonic-assisted magnetic abrasive finishing as functions of the supply voltage, working gap and concentration of abrasive particles in a flexible magnetic abrasive brush. A comparison of theoretical and experimental results is performed to validate the proposed model

Multi-objective optimization of ultrasonic-assisted magnetic abrasive finishing process

Ultrasonic-assisted magnetic abrasive finishing (UAMAF) is an advanced abrasive finishing process that finishes a workpiece surface effectually when compared to a traditional magnetic abrasive finishing process in the order of nanometer. A change of surface roughness and material removal rate are two important factors determining the efficacy of the process. These two factors affect the surface quality and production time and, thereby, a total production cost. The finishing performed at higher material removal rates leads to a loss in shape/form accuracy of the surface. At the same time, increasing the rate of change of surface roughness increases loss of material. For an optimized finishing process, a compromise has to be made between the change of surface roughness and the material removal (loss). In this work, a multi-objective optimization technique based on genetic algorithm is used to optimize the finishing parameters in the UAMAF processes. A fuzzy-set-based strategy for a higher level decision is also discussed. The results of the optimization based on a mathematical model of the process are validated with the experimental results and are found to be in compliance

Modeling of normal force and finishing torque considering shearing and ploughing effects in ultrasonic assisted magnetic abrasive finishing process with sintered magnetic abrasive powder

Ultrasonic assisted magnetic abrasive finishing process (UAMAF) is a precision manufacturing process that results nano-scale level finish in a part. Normal force on a particle helps indenting the particle in the work surface whereas horizontal force provides finishing torque that in-turn helps the particle to perform micro-machining. Better understanding of the effect of these forces on material removal and wear pattern of the work-piece necessitates mathematical modeling of normal force and finishing torque and subsequently its validation with experimental results. In the present study, single particle interaction concept is considered to develop a model which is subsequently applied for all active particles of magnetic abrasive powder (MAP). Separation point theory is applied to consider the effect of ploughing below a critical depth and shearing above that depth. Normal components of shearing and ploughing forces are considered for calculating normal force and horizontal components of shearing and ploughing forces are taken to calculate finishing torque. Johnson-Cook model is applied to calculate shearing strength of the work material during UAMAF. The impact of ultrasonic vibrations is considered while calculating strain rate. Images are taken with the help of scanned electron microscope and atomic force microscope to study the material removal and wear mechanism during UAMAF process. Predicted values of force and torque model are validated with the experimental values

A review: On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics