Design and analysis of Intelligent Navigational controller for Mobile Robot

Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner

Intelligent Trajectory Planning and Navigational Analysis of Wheeled Mobile Robot in Cluttered Workspace

Path planning and navigation analysis of wheeled mobile robots have received a significant role in the field of robotics by researchers in the past few decades. The research proposed by the researchers previously related to mobile robotics mainly considered three aspects such as localization, map building and path-planning. These aspects played an important role in finding out the feasible navigational path and it is also responsible for smooth or safe navigation in an environment. By keeping all these aspects in mind related to the solution of the trajectory planning problems, the trajectory planning and navigation control algorithms are presented in this proposed work. The proposed algorithms (BNN, DAYINDI AI, AGSA-DAYINDI AI and PSO-DAYINDI AI) can learn from the search space and configure themselves with the help of sensor modules. Based on this principle, the robot generates a collision-free path by avoiding obstacles from the source to the target. The primary objective of this research work is to design and develop smart computational intelligence techniques that addresses the online navigation problems as well as solves the problems using its learning feature. In this work, the path planning problems are addressed for unknown and messy environments using developed AI techniques. Two individual computational intelligence methodologies have been developed based on the Behaviour Based Neural Network (BNN) and DAYINDI AI algorithm. Also, hybrid methodologies have been developed by integrating the AGSA with DAYINDI AI and PSO with DAYINDI AI, to solve the mobile robot navigation problems. The performances of the techniques are examined individually, through simulation and real-time experiments. During the comparative study, good agreements (average deviation is less than 6%) have been found between simulation and real-time experiments. It has been noticed that AGSA-DAYINDI (deviation is less than 6%) and PSO-DAYINDI (deviation is less than 5.5%) hybrid controllers execute better results as compared to BNN (deviation is less than 6%) and DAYINDI AI (deviation is less than 6.5%) controllers. The navigational results obtained from the developed techniques are validated by comparing with the results of existing navigational techniques such as Fuzzy logic, Neuro-Fuzzy, Behavior-based Fuzzy, Heuristic approach, Heterogeneous ACO and SACOdm techniques. In comparison studies, it is found that the proposed methodologies generate better navigational results as compared to above existing techniques

An Analytical survey of Stock trading & Investor Preferences, with Special reference to rural areas of Jaipur District

<p>The Indian economy aspires to become a 5 trillion dollar economy by the year 2025. In making the Indian economy a 5 trillion dollar economy the role of rural investors will be of utmost importance. The  author  has noticed that the education,  skill development, better infrastructure, access to markets, credit availability in rural areas and liberalization of the State's economy as the key factors are responsible for  steadily shifting  of  rural  workforce  in  favour  of  non-farm activities. This study will be represents on evolution and impact of stock trading in rural areas of Jaipur District. </p&gt

Mobile robot navigation in unknown static environments using ANFIS controller

Navigation and obstacle avoidance are the most important task for any mobile robots. This article presents the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for mobile robot navigation and obstacle avoidance in the unknown static environments. The different sensors such as ultrasonic range finder sensor and sharp infrared range sensor are used to detect the forward obstacles in the environments. The inputs of the ANFIS controller are obstacle distances obtained from the sensors, and the controller output is a robot steering angle. The primary objective of the present work is to use ANFIS controller to guide the mobile robot in the given environments. Computer simulations are conducted through MATLAB software and implemented in real time by using C/C++ language running Arduino microcontroller based mobile robot. Moreover, the successful experimental results on the actual mobile robot demonstrate the effectiveness and efficiency of the proposed controller

<span style="mso-bidi-font-family:Times-Bold;mso-bidi-language:HI">Interactive effects of elevated CO₂ and phosphorus nutrition on growth and phosphorus utilization efficiency in wheat and rye </span>

93-100An attempt was made to study the interactive effects of phosphorus (P) nutrition and elevated CO<span style="mso-bidi-font-family:Times-Bold;mso-bidi-language: HI">2<span style="mso-bidi-font-family:Times-Roman;mso-bidi-language: HI"> on growth and P-utilization efficiency (PUE) in wheat and rye. Wheat (PBW-396, PDW-233) and rye (WSP-540-2) were grown at low (2 μM) and sufficient (500 μM) P under ambient (380±10 μmol mol-1, aCO2) and elevated CO2 concentrations (700 μmol mol-1, eCO<span style="mso-bidi-font-family:Times-Bold; mso-bidi-language:HI">2<span style="mso-bidi-font-family:Times-Roman; mso-bidi-language:HI">). Results revealed that shoot, root and total plant dry matter accumulation and partitioning were significantly influenced by CO2 and P levels. eCO<span style="mso-bidi-font-family:Times-Bold;mso-bidi-language: HI">2<span style="mso-bidi-font-family:Times-Roman;mso-bidi-language: HI"> increased shoot dry matter up to 27% in PDW-233 compared to aCO2 plants grown under sufficient P. Partitioning of dry matter towards root was higher in plants raised under eCO<span style="mso-bidi-font-family:Times-Bold; mso-bidi-language:HI">2<span style="mso-bidi-font-family:Times-Roman; mso-bidi-language:HI"> with low-P resulting in maximum root-to-shoot ratio. Total leaf area was 71% higher in rye under eCO2 with sufficient P compared to aCO<span style="mso-bidi-font-family:Times-Bold; mso-bidi-language:HI">2<span style="mso-bidi-font-family:Times-Roman; mso-bidi-language:HI">. Significantly higher lateral root density, length and surface area were noted in plants grown at low-P under eCO2 as compared to aCO<span style="mso-bidi-font-family:Times-Bold;mso-bidi-language: HI">2<span style="mso-bidi-font-family:Times-Roman;mso-bidi-language: HI">. The total P uptake was increased by 70% when plants were raised with sufficient P under eCO<span style="mso-bidi-font-family:Times-Bold; mso-bidi-language:HI">2<span style="mso-bidi-font-family:Times-Roman; mso-bidi-language:HI"> in comparison to aCO<span style="mso-bidi-font-family: Times-Bold;mso-bidi-language:HI">2<span style="mso-bidi-font-family: Times-Roman;mso-bidi-language:HI"> while the PUE increased by 26% in response to CO<span style="mso-bidi-font-family:Times-Bold;mso-bidi-language: HI">2<span style="mso-bidi-font-family:Times-Roman;mso-bidi-language: HI"> enrichment at low-P. Among cereals grown at low-P, highest PUE was observed in PBW-396 in response to elevated CO2. These finding suggests that cereals would be more responsive to P nutrition and efficient in its utilization even at low-P conditions under rising atmospheric CO2 levels. </span

Phytostabilization of coal mine overburden waste, exploiting the phytoremedial efficacy of lemongrass under varying level of cow dung manure

A pot study was performed to assess the phytoremedial potential of Cymbopogon citratus (D.C.) Staf. for reclamation of coal mine overburden dump wastes, emphasizing the outcome of amendment practices using cow dung manure (CM) and garden soil mixtures on the revegetation of over-burden wastes (OB). Wastes amendment with cow dung manure and garden soil resulted in a significant increase in soil health and nutrient status along with an increment in the phytoavailability of Zn and Cu which are usually considered as micronutrients, essential for plant growth. A significant increment in the total biomass of lemongrass by 38.6% under CM20 (OB: CM 80:20) was observed along with improved growth parameters under amended treatments as compared to OB (100% waste). Furthermore, the proportionate increases in the assimilative rate, water use efficiency, and chlorophyll fluorescence have been observed with the manure application rates. Lemongrass emerged out to be an efficient metal-tolerant herb species owing to its high metal-tolerance index (>100%). Additionally, lemongrass efficiently phytostablized Pb and Ni in the roots. Based on the strong plant performances, the present study highly encourages the cultivation of lemongrass in coal mining dumpsites for phytostabilization coupled with cow-dung manure application (20% w/w)

Investigation of crystallinity, mechanical properties, fracture toughness and cell proliferation in plasma sprayed graphene nano platelets reinforced hydroxyapatite coating

Graphene nanoplatelets (GNPs) (0, 1 wt% and 2 wt%) reinforced hydroxyapatite (HA), denoted by HA, HA-1G and HA-2G respectively, coatings were fabricated on titanium substrate (Ti-6Al-4V) through atmospheric plasma spraying. The major parameters such as porosity, crystallinity, mechanical properties, toughness and cell proliferation were manipulated by varying plasma power from 15 kW to 35 kW and content of GNPs. For the coating synthesized at all plasma power, GNPs were found to be retained by Raman spectroscopy. GNPs reinforcement has led to an improvement in the crystallinity of the composite coatings as compared to HA coatings. On the contrary to it, increase in plasma power from 15 kW to 35 kW resulted in decrease in crystallinity for all three individual coating. Further, Increment in plasma power from 15 kW to 35 kW delivered a significant enhancement in hardness, elastic modulus and fracture toughness up to 81%, 149% and 282% respectively for HA-1 wt% GNPs coating, while it improved to 20%, 50% and 173% respectively on the addition of 2 wt% GNPs in HA coating fabricated at 35 kW. Enhancement in hardness, elastic modulus and fracture toughness was due to three simultaneous reasons: (1) Reduction in porosity (2) Uniform dispersion of GNPs and (3) Toughening mechanism offered by GNPs. Further, the addition of GNPs showed a remarkable improvement in the rate of cell proliferation in the HA coating. A detailed discussion over the reasons behind every results have been made profoundly

Morphological characterization and evaluation of soybean genotypes under rainfed ecosystem of Nepal

Morphological description of soybean genotypes useful in soybean improvement program. The objective of this research was to identify genotypes with high grain yield and desirable agronomic traits as well as stability across environments. A set of 25 soybean genotypes were used to evaluate under alpha lattice design with two replications at research farm of National Oilseed Research Program, Nawalpur, Sarlahi; National Grain Legumes Research Program Khajura, Banke, and Institute of Agriculture and Animal Science, Sundarbazar, Lamjung during July to November of 2018. Result showed that soybean accessions exhibited morphological variation in qualitative traits. The soybean landrace Kailali-3 had a significantly higher grain yield (1.7 ton ha-1). The result of GGE biplots indicated Kavre, Kailali-3 and Lekali Bhatta as the most stable genotypes in all environments. CO 164 was the highest-yielding genotype with above mean average yield at all tested environments. On the other hand, additive main-effects and multiplicative interaction (AMMI) Analysis revealed Chitwan-9 and Palpa white as the most stable due to the low IPC1 scores and moderate mean yield