Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

A spatially-explicit decision support system for invasive weed species management

Invasive weed species are a recognized problem worldwide causing economic and environmental problems. Management of weeds is complex and challenging because of multiple decisions that need to be made when allocating limited resources to control current infestation areas including which weeds to treat, where to treat, how to treat, and when to treat. Models have been developed to simulate weed spread, however they lack the ability to simulate the short term effects of weed treatments and analyze trade-offs among control allocation options. This trade-off analysis is critical in developing cost-efficient treatment decisions especially when available budget for treatments is limited. To address the limitations of traditional weed treatment planning and provide weed mangers with a decision support tool that can enhance their decision-making process, a spatially-explicit decision support system was developed. Based on current infestation areas, treatment effects estimation, and vegetation susceptibility, the system simulates weed spread across the landscape, and develops a five-year treatment plan that minimizes total infestation area over time

Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms

An Integrated Method for Optimizing Bridge Maintenance Plans

Bridges are one of the vital civil infrastructure assets, essential for economic developments and public welfare. Their large numbers, deteriorating condition, public demands for safe and efficient transportation networks and limited maintenance and intervention budgets pose a challenge, particularly when coupled with the need to respect environmental constraints. This state of affairs creates a wide gap between critical needs for intervention actions, and tight maintenance and rehabilitation funds. In an effort to meet this challenge, a newly developed integrated method for optimized maintenance and intervention plans for reinforced concrete bridge decks is introduced. The method encompasses development of five models: surface defects evaluation, corrosion severities evaluation, deterioration modeling, integrated condition assessment, and optimized maintenance plans. These models were automated in a set of standalone computer applications, coded using C#.net in Matlab environment. These computer applications were subsequently combined to form an integrated method for optimized maintenance and intervention plans. Four bridges and a dataset of bridge images were used in testing and validating the developed optimization method and its five models. The developed models have unique features and demonstrated noticeable performance and accuracy over methods used in practice and those reported in the literature. For example, the accuracy of the surface defects detection and evaluation model outperforms those of widely-recognized machine leaning and deep learning models; reducing detection, recognition and evaluation of surface defects error by 56.08%, 20.2% and 64.23%, respectively. The corrosion evaluation model comprises design of a standardized amplitude rating system that circumvents limitations of numerical amplitude-based corrosion maps. In the integrated condition, it was inferred that the developed model accomplished consistent improvement over the visual inspection procedures in-use by the Ministry of Transportation in Quebec. Similarly, the deterioration model displayed average enhancement in the prediction accuracies by 60% when compared against the most commonly-utilized weibull distribution. The performance of the developed multi-objective optimization model yielded 49% and 25% improvement over that of genetic algorithm in a five-year study period and a twenty five-year study period, respectively. At the level of thirty five-year study period, unlike the developed model, classical meta-heuristics failed to find feasible solutions within the assigned constraints. The developed integrated platform is expected to provide an efficient tool that enables decision makers to formulate sustainable maintenance plans that optimize budget allocations and ensure efficient utilization of resources

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks

Genetic structure of invasive baby’s breath (Gypsophila paniculata) populations in a Michigan dune system

Invasive species can reduce biodiversity of a system by outcompeting native species for resources, changing the physical characteristics of a habitat, and altering natural disturbance regimes. Coastal sand dune ecosystems are dynamic with elevated levels of disturbance, and as such they are highly susceptible to plant invasions. The topography, geographic distribution of preferred habitat, and disturbance regime in an ecosystem can influence where an invasive plant becomes established, its dispersal patterns, and how densely it grows. One such invasion that is of major concern to the Great Lakes dune systems is baby’s breath (Gypsophila paniculata). The invasion of baby’s breath negatively impacts native species, including rare ones such as Pitcher’s thistle (Cirsium pitcheri). Estimating the genetic variation and structure of invasive populations can lead to a better understanding of the invasion history, and the factors influencing invasion success. Microsatellite genetic markers can be beneficial for estimating levels of diversity present within and among populations. Our research goals were to develop microsatellite primers to analyze invasive populations, quantify the genetic diversity and estimate the genetic structure of these invasive populations of baby’s breath in the Michigan dune system. We identified 16 polymorphic nuclear microsatellite loci for baby’s breath out of 73 loci that successfully amplified from a primer library created using Illumina sequencing technology. We analyzed 12 populations at 14 nuclear and 2 chloroplast microsatellite loci and found moderate genetic diversity, strong genetic structure among the populations (global FST = 0.228), and also among two geographic regions that are separated by the Leelanau peninsula. Results from a Bayesian clustering analysis suggest two main population clusters. Isolation by distance was found over all 12 populations (R = 0.755, P \u3c 0.001) and when only cluster 2 populations were included (R = 0.523, P = 0.030); populations within cluster 1 revealed no significant relationship (R = 0.205, P = 0.494). The results suggest the possibility of at least two separate introduction events to Michigan. These results provide an understanding of the invasion history and factors contributing to invasion success. Management of invasive populations can use this to identify populations of high priority

Practical guidelines for modelling post-entry spread in invasion ecology

In this article we review a variety of methods to enable understanding and modelling the spread of a pest or pathogen post-entry. Building upon our experience of multidisciplinary research in this area, we propose practical guidelines and a framework for model development, to help with the application of mathematical modelling in the field of invasion ecology for post-entry spread. We evaluate the pros and cons of a range of methods, including references to examples of the methods in practice. We also show how issues of data deficiency and uncertainty can be addressed. The aim is to provide guidance to the reader on the most suitable elements to include in a model of post-entry dispersal in a risk assessment, under differing circumstances. We identify both the strengths and weaknesses of different methods and their application as part of a holistic, multidisciplinary approach to biosecurity research

Semantic Segmentation based deep learning approaches for weed detection

Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of ‘Focal loss’ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications Advisor: Yeyin Sh

Actin machinery and mechanosensitivity in invadopodia, podosomes and focal adhesions.

International audienceThe invasiveness of cells is correlated with the presence of dynamic actin-rich membrane structures called invadopodia, which are membrane protrusions that are associated with localized polymerization of sub-membrane actin filaments. Similar to focal adhesions and podosomes, invadopodia are cell-matrix adhesion sites. Indeed, invadopodia share several features with podosomes, but whether they are distinct structures is still a matter of debate. Invadopodia are built upon an N-WASP-dependent branched actin network, and the Rho GTPase Cdc42 is involved in inducing invadopodial-membrane protrusion, which is mediated by actin filaments that are organized in bundles to form an actin core. Actin-core formation is thought to be an early step in invadopodium assembly, and the actin core is perpendicular to the extracellular matrix and the plasma membrane; this contrasts with the tangential orientation of actin stress fibers anchored to focal adhesions. In this Commentary, we attempt to summarize recent insights into the actin dynamics of invadopodia and podosomes, and the forces that are transmitted through these invasive structures. Although the mechanisms underlying force-dependent regulation of invadopodia and podosomes are largely unknown compared with those of focal adhesions, these structures do exhibit mechanosensitivity. Actin dynamics and associated forces might be key elements in discriminating between invadopodia, podosomes and focal adhesions. Targeting actin-regulatory molecules that specifically promote invadopodium formation is an attractive strategy against cancer-cell invasion