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

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

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

Views of professional stakeholders on readiness for a safe road system in Nepal; an exploratory qualitative study

Road traffic injuries in Nepal are increasing despite being largely preventable. Little evidence exists regarding the barriers and facilitators to a safer road system. This study aimed to explore the perspectives of professionals whose jobs had the potential to influence road safety in Nepal regarding challenges and potential solutions. Semi-structured interviews with eight informants from diverse roles were analysed thematically. Three themes were identified: Modifying behaviours of road users; Road planning, construction and maintenance; and the Governance of roads and traffic. All participants considered the primary cause of crashes to be the negligent behavior of the road users, suggesting that improved knowledge would influence their decisions. Poor road design, building and maintenance, together with poor vehicle standards, and lack of investment and enforcement of existing road safety legislation, needed to be addressed through greater coordination of the agencies. The study identified a range of areas for future inquiry and action

Respiratory carriage of hypervirulent Klebsiella pneumoniae by indigenous populations of Malaysia

Klebsiella pneumoniae is a Gram-negative Enterobacteriaceae that is classified by the World Health Organisation (WHO) as a Priority One ESKAPE pathogen. South and Southeast Asian countries are regions where both healthcare associated infections (HAI) and community acquired infections (CAI) due to extended-spectrum β-lactamase (ESBL)-producing and carbapenem-resistant K. pneumoniae (CRKp) are of concern. As K. pneumoniae can also exist as a harmless commensal, the spread of resistance genotypes requires epidemiological vigilance. However there has been no significant study of carriage isolates from healthy individuals, particularly in Southeast Asia, and specially Malaysia. Here we describe the genomic analysis of respiratory isolates of K. pneumoniae obtained from Orang Ulu and Orang Asli communities in Malaysian Borneo and Peninsular Malaysia respectively. The majority of isolates were K. pneumoniae species complex (KpSC) 1 K. pneumoniae (n = 53, 89.8%). Four Klebsiella variicola subsp. variicola (KpSC3) and two Klebsiella quasipneumoniae subsp. similipneumoniae (KpSC4) were also found. It was discovered that 30.2% (n = 16) of the KpSC1 isolates were ST23, 11.3% (n = 6) were of ST65, 7.5% (n = 4) were ST13, and 13.2% (n = 7) were ST86. Only eight of the KpSC1 isolates encoded ESBL, but importantly not carbapenemase. Thirteen of the KpSC1 isolates carried yersiniabactin, colibactin and aerobactin, all of which harboured the rmpADC locus and are therefore characterised as hypervirulent. Co-carriage of multiple strains was minimal. In conclusion, most isolates were KpSC1, ST23, one of the most common sequence types and previously found in cases of K. pneumoniae infection. A proportion were hypervirulent (hvKp) however antibiotic resistance was low