Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

Model and Design of a Power Driver for Piezoelectric Stack Actuators

A power driver has been developed to control piezoelectric stack actuators used in automotive application. A FEM model of the actuator has been implemented starting from experimental characterization of the stack and mechanical and piezoelectric parameters. Experimental results are reported to show a correct piezoelectric actuator driving method and the possibility to obtain a sensor-less positioning contro

Human-Centered Navigation and Person-Following with Omnidirectional Robot for Indoor Assistance and Monitoring

Robot assistants and service robots are rapidly spreading out as cutting-edge automation solutions to support people in their everyday life in workplaces, health centers, and domestic environments. Moreover, the COVID-19 pandemic drastically increased the need for service technology to help medical personnel in critical conditions in hospitals and domestic scenarios. The first requirement for an assistive robot is to navigate and follow the user in dynamic environments in complete autonomy. However, these advanced multitask behaviors require flexible mobility of the platform to accurately avoid obstacles in cluttered spaces while tracking the user. This paper presents a novel human-centered navigation system that successfully combines a real-time visual perception system with the mobility advantages provided by an omnidirectional robotic platform to precisely adjust the robot orientation and monitor a person while navigating. Our extensive experimentation conducted in a representative indoor scenario demonstrates that our solution offers efficient and safe motion planning for person-following and, more generally, for human-centered navigation tasks

RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following

Trade-off analysis and design of a Hydraulic Energy Scavenger

In the last years there has been a growing interest in intelligent, autonomous devices for household applications. In the near future this technology will be part of our society; sensing and actuating will be integrated in the environment of our houses by means of energy scavengers and wireless microsystems. These systems will be capable of monitoring the environment, communicating with people and among each other, actuating and supplying themselves independently. This concept is now possible thanks to the low power consumption of electronic devices and accurate design of energy scavengers to harvest energy from the surrounding environment. In principle, an autonomous device comprises three main subsystems: an energy scavenger, an energy storage unit and an operational stage. The energy scavenger is capable of harvesting very small amounts of energy from the surroundings and converting it into electrical energy. This energy can be stored in a small storage unit like a small battery or capacitor, thus being available as a power supply. The operational stage can perform a variety of tasks depending on the application. Inside its application range, this kind of system presents several advantages with respect to regular devices using external energy supplies. They can be simpler to apply as no external connections are needed; they are environmentally friendly and might be economically advantageous in the long term. Furthermore, their autonomous nature permits the application in locations where the local energy grid is not present and allows them to be ‘hidden' in the environment, being independent from interaction with humans. In the present paper an energy-harvesting system used to supply a hydraulic control valve of a heating system for a typical residential application is studied. The system converts the kinetic energy from the water flow inside the pipes of the heating system to power the energy scavenger. The harvesting unit is composed of a hydraulic turbine that converts the kinetic energy of the water flow into rotational motion to drive a small electric generator. The design phases comprise a trade-off analysis to define the most suitable hydraulic turbine and electric generator for the energy scavenger, and an optimization of the components to satisfy the systems specification

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks

Harnessing the Automotive Waste Heat with Thermoelectric Modules Using Maximum Power Point Tracking Method

The present work shows a comprehensive methodology and design steps to recover energy from the automotive waste heat. A thermoelectric generator must be connected to a power converter in order to extract the maximum power from the generator and, also, satisfy different constrains to charge a battery. Starting from the electrical model of thermoelectric cells, it is evaluated their combination to realize a thermoelectric generator (TEG) comply with the automotive regulation, then considering input/output electric characteristics, it is evaluated the best converter topology to satisfy all constrains. Design steps and power dissipation estimation are deeply explained. TEG and power converter models are simulated in a model-based environment to allow the design of the control algorithms. The control system consists of nested control loops. Two maximum power point tracking (MPPT) algorithms are evaluated. The MPPT output is used as reference for a current control loop. The maximum power characteristic of a TEG has a quadratic behavior and working without the tracking of the maximum power point could drastically decrease the generated power from the TEG and the system efficiency. There are presented simulation results of the control algorithms and experimental data are shown in order to validate the design steps

Visual-Inertial Indoor Navigation Systems and Algorithms for UAV Inspection Vehicles

In UAV navigation, one of the challenges in which considerable efforts are being focused is to be able to move indoors. Completing this challenge would imply being able to respond to a series of industrial market needs such as the inspection of internal environments for safety purpose or the inventory of stored material. Usually GPS is used for navigation, but in a closed or underground environment, its signal is almost never available. As a consequence, to achieve the goal and ensure that the UAV is able to accurately estimate its position and orientation without the usage of GPS, an alternative navigation system based on visual-inertial algorithms and the SLAM will be proposed using data fusion techniques. In addition to the navigation system, we propose an obstacle avoidance method based on a Lidar sensor that allows navigation even in the absence of light

A cloud robotics architecture for an emergency management and monitoring service in a smart cityenvironment

Cloud robotics is revolutionizing not only the robotics industry but also the ICT world, giving robots more storage and computing capacity, opening new scenarios that blend the physical to the digital world. In this vision new IT architectures are required to manage robots, retrieve data from them and create services to interact with users. In this paper a possible implementation of a cloud robotics architecture for the interaction between users and UAVs is described. Using the latter as monitoring agents, a service for fighting crime in urban environment is proposed, making one step forward towards the idea of smart cit