Effective Target Aware Visual Navigation for UAVs

In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-projection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR) 201

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201

Plane extraction for indoor place recognition

In this paper, we present an image based plane extraction method well suited for real-time operations. Our approach exploits the assumption that the surrounding scene is mainly composed by planes disposed in known directions. Planes are detected from a single image exploiting a voting scheme that takes into account the vanishing lines. Then, candidate planes are validated and merged using a region grow- ing based approach to detect in real-time planes inside an unknown in- door environment. Using the related plane homographies is possible to remove the perspective distortion, enabling standard place recognition algorithms to work in an invariant point of view setup. Quantitative Ex- periments performed with real world images show the effectiveness of our approach compared with a very popular method

An Effective Multi-Cue Positioning System for Agricultural Robotics

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters, 201

Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2018

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201

Water–Demand Management in the Kingdom of Saudi Arabia for Enhancement Environment

The purpose and the goal of the paper is growing substantially and that is being met through the available scarce and dwindling water resources. The kingdom of Saudi Arabia (KSA) faces an acute water shortage due to arid climate and absence of permanent lakes and rivers. Ever-increasing imbalances are usually met by increasing water supplies, whereas the concepts of water-demand management have not been given due importance and weight age. Meeting the rapidly rising demand with scarce and depleting resources remains the critical issue. The goal of this paper is showing; how Geographical Information Systems(GIS) can be used to support infrastructure  planners and analyst  on a local area. This paper places emphasizes on the urgency of adopting conservation and water-demand management initiatives to maintain demand supply relationship and achieve an acceptable balance between water needs and availability. The kingdom places emphasis on the shift from supply development to demand management to use of critical and non-renewable water resources efficiently. The paper suggests that the water-use-efficiency (WUE) in various sectors can be enhanced and improved in the kingdom. The paper presents an overview of the country’s water resources and issues related to water. Some possible conservation and remedial measures particularly in the agricultural sector-the largest and most inefficient user of water have been suggested. The objective of this paper is to safeguard and conserve this precious natural resource through environmental friendly technologies for the future generations to come. It is presumed that water resources can be managed on sustainable basis by devising and employing environmental friendly technologies including water conservation measures. The usefulness of these measures can be supplemented through the vibrant and viable extension and education initiatives and capacity building programs. In this work, three sets applications of GIS models have been produced. The geodatabase of district  areas  in Saudi Arabia including these layers of  Area, Subarea, Cites, water in land, water area, land cover, roads, rail roads, elevations. Keywords: Water Demand, Water Resources, GIS, Highway Street, XML Schema

An Assessment of Rewards and Motivation Strategies as Predictors of Employee Job Satisfaction in the Banking Industry in Kenya

This study sought to assess the prediction effect of reward and Motivation strategies on job satisfaction in the Commercial Banks in Kenya. The sample of the study comprised of 78 respondents being 28 managerial staff and 50 line employees drawn from Commercial Banks in Western Kenya. Data was collected by use of questionnaires and interview schedule. Statistically quantitative data was analyzed using descriptive as well as inferential statistics. Study findings revealed a statistically significant relationship between employee reward and job satisfaction and a significant relationship between employee motivation and job satisfaction. Findings of this study have provided vital and relevant information to stakeholders in the banking industry in Kenya and beyond on how reward and motivation strategies can be harnessed to bring about employee job satisfaction for improved organizational performance. The study has also stretched the frontiers of knowledge on the relationship between employee motivation and resultant occupational attitudes. Keywords: Rewards, Motivation, Job Satisfaction, Commercial Bank

Is it possible to predict the success of non-invasive positive pressure ventilation in acute respiratory failure due to COPD?

AbstractThere is now sufficient evidence that non-invasive positive pressure ventilation (NIPPV) in selected patients with severe hypercapnic acute respiratory failure due to chronic obstructive pulmonary disease (COPD) is more effective than pharmacological therapy alone. The aim of this study was to identify prognostic factors to predict the success of this technique. Fifty-nine consecutive patients with COPD admitted to a respiratory ward for 75 episodes of acute respiratory failure treated with NIPPV were analysed: success (77%) or failure (23%) were evaluated by survival and the need for endotracheal intubation. There were no significant differences in age, sex, cause of relapse and lung function tests between the two groups. Patients in whom NIPPV was unsuccessful were significantly underweight, had an higher Acute Physiology and Chronic Health Evaluation (APACHE) II score, and a lower serum level of albumin in comparison with those in whom NIPPV was successful. They demonstrated significantly greater abnormalities in pH and P a CO2at baseline and after 2 h of NIPPV. The logistic regression analysis demonstrated that, when all the variables were tested together, a high APACHE II score and a low albumin level continued to have a significant predictive effect. This analysis could predict the outcome in 82% of patients. In conclusion, our study suggests that low albumin serum levels and a high APACHE II score may be important indices in predicting the success of NIPPV