Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features

Flexible system of multiple RGB-D sensors for measuring and classifying fruits in agri-food Industry

The productivity of the agri-food sector experiences continuous and growing challenges that make the use of innovative technologies to maintain and even improve their competitiveness a priority. In this context, this paper presents the foundations and validation of a flexible and portable system capable of obtaining 3D measurements and classifying objects based on color and depth images taken from multiple Kinect v1 sensors. The developed system is applied to the selection and classification of fruits, a common activity in the agri-food industry. Being able to obtain complete and accurate information of the environment, as it integrates the depth information obtained from multiple sensors, this system is capable of self-location and self-calibration of the sensors to then start detecting, classifying and measuring fruits in real time. Unlike other systems that use specific set-up or need a previous calibration, it does not require a predetermined positioning of the sensors, so that it can be adapted to different scenarios. The characterization process considers: classification of fruits, estimation of its volume and the number of assets per each kind of fruit. A requirement for the system is that each sensor must partially share its field of view with at least another sensor. The sensors localize themselves by estimating the rotation and translation matrices that allow to transform the coordinate system of one sensor to the other. To achieve this, Iterative Closest Point (ICP) algorithm is used and subsequently validated with a 6 degree of freedom KUKA robotic arm. Also, a method is implemented to estimate the movement of objects based on the Kalman Filter. A relevant contribution of this work is the detailed analysis and propagation of the errors that affect both the proposed methods and hardware. To determine the performance of the proposed system the passage of different types of fruits on a conveyor belt is emulated by a mobile robot carrying a surface where the fruits were placed. Both the perimeter and volume are measured and classified according to the type of fruit. The system was able to distinguish and classify the 95% of fruits and to estimate their volume with a 85% of accuracy in worst cases (fruits whose shape is not symmetrical) and 94% of accuracy in best cases (fruits whose shape is more symmetrical), showing that the proposed approach can become a useful tool in the agri-food industry.This project has been supported by the National Commission for Science and Technology Research of Chile (Conicyt) under FONDECYT grant 1140575 and the Advanced Center of Electrical and Electronic Engineering - AC3E (CONICYT/FB0008)

Machinery for potato harvesting: a state-of-the-art review

Potatoes are the fourth most important crop for human consumption. In the 18 century, potatoes saved the European population from starvation, and since then, it has become one of the primary crops cultivated in countries such as Spain, France, Germany, Ukraine and the United Kingdom. Potato production worldwide reached 368.8 million tonnes in 2019, 371.1 million tonnes in 2020, and 376.1 million tonnes in 2021, with production expected to grow alongside the worldwide population. However, the agricultural sector is currently suffering from urbanization. With the next generation of farmers relocating to cities, there is a diminishing and ageing agricultural workforce. Consequently, farms urgently need innovation, particularly from a technology perspective. As a result, this work is focused on reviewing the worldwide developments in potato harvesting, with an emphasis on mechatronics, the use of intelligent systems and the opportunities that arise from applications utilising the Internet of Things (IoT). Our work covers worldwide scientific publications in the last five years, sustained by public data made available from different governments. We end our review by providing a discussion on the future trends derived from our analysis

Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pereira, Fernando M. Lobo. Universidad de Porto; PortugalFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Collision-free navigation of N-trailer vehicles with motion constraints

In this work, a collision-free navigation strategy for N-trailer vehicles is proposed. This approach is based on a scalable cascaded control scheme to perform several tasks simultaneously: trajectory tracking control, off-track error reduction, external obstacles avoidance, and inter-vehicle collision avoidance. To validate the proposed strategy, a Generalized N-trailer (GNT) structure with a car-like tractor and 10 trailers is tested in simulation to track an U-shape trajectory in presence of unknown obstacles, similar to the trajectories that agricultural vehicles must perform in real applications. The well-known information about external infrastructure is also considered to reduce unsafe trailers off-track errors in turning scenarios. Moreover, the motion constraints imposed by the car-like tractor physical limitations and the interconnections between trailers are also considered by restricting the control input in order to avoid collision between trailers. The simulation results obtained showed a safe navigation which performed feasible maneuvers without collisions between the vehicles' chain and any trailer or external obstacle

A comprehensive performance evaluation of different mobile manipulators used as displaceable 3D printers of building elements for the construction industry

The construction industry is currently technologically challenged to incorporate new developments for enhancing the process, such as the use of 3D printing for complex building structures,which is the aim of this brief. To do so, we show a systematic study regarding the usability and performance of mobile manipulators as displaceable 3D printing machinery in construction sites,with emphasis on the three main different existing mobile platforms: the car-like, the unicycleand the omnidirectional (mecanum wheeled), with an UR5 manipulator on them. To evaluate its performance, we propose the printing of the following building elements: helical, square, circular and mesh, with different sizes. As metrics, we consider the total control effort observed in the robots and the total tracking error associated with the energy consumed in the activity to get a more sustainable process. In addition, to further test our work, we constrained the robot workspace thus resemblingreal life construction sites. In general, the statistical results show that the omnidirectional platform presents the best results –lowest tracking error and lowest control effort– for circular, helicoidal and mesh building elements; and car-like platform shows the best results for square-like building element. Then,an innovative performance analysis is achieved for the printing of building elements, with a contribution to the reduction of energy consumptio

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future

Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work