Several countries encourage the practice of football for rehabilitation and social inclusion purposes. For visually impaired people, football is purely sound-based, where the ball and the players are ...constantly emitting sounds for localization purposes in the field. However, the task of shooting the ball requires of a non-visually impaired extra person, behind the goal (known as caller), whom is punching the four corner of such goal to help the athletes. The presence of the caller restricts the self-sufficiency of the players. This work addresses such problem, by presenting a goal for visually impaired players with the aim of enhancing their self-sufficiency.
The electronic goal is designed with four functionalities for training purposes, by returning sound-based feedback of its position and the places where the ball has impacted. The system is validated with seven volunteers from Chilean Football Soccer National Team. A questionnaire was answered by the players before and after the tests to statically validate the proposed device.
The presented system is portable and designed following a modular criterion suitable for visually impaired people self-assembling. From a test of 350 shootings, the electronic goal showed to enhance the shooting assertiveness from 82 to 92%, and the accuracy from 20 to 56% compared to the traditional caller.
The electronic goal showed to enhance the self-sufficiency of athletes, by improving their assertiveness in shooting training. Nevertheless, and according to the responses to the questionnaires, the system needs improvements in its portability and handling.
•An error propagation analysis of GNSS over the laser scan measurements.•A scan matching approach fused with GNSS measurements.•Analysis of the crown surface area, crown volume, and crown porosity.
...Currently, 3D point clouds are obtained via LiDAR (Light Detection and Ranging) sensors to compute vegetation parameters to enhance agricultural operations. However, such a point cloud is intrinsically dependent on the GNSS (global navigation satellite system) antenna used to have absolute positioning of the sensor within the grove. Therefore, the error associated with the GNSS receiver is propagated to the LiDAR readings and, thus, to the crown or orchard parameters. In this work, we first describe the error propagation of GNSS over the laser scan measurements. Second, we present our proposal to overcome this effect based only on the LiDAR readings. Such a proposal uses a scan matching approach to reduce the error associated with the GNSS receiver. To accomplish such purpose, we fuse the information from the scan matching estimations with the GNSS measurements. In the experiments, we statistically analyze the dependence of the grove parameters extracted from the 3D point cloud -specifically crown surface area, crown volume, and crown porosity- to the localization error. We carried out 150 trials with positioning errors ranging from 0.01 meters (ground truth) to 2 meters. When using only GNSS as a localization system, the results showed that errors associated with the estimation of vegetation parameters increased more than 100 % when positioning error was equal or bigger than 1 meter. On the other hand, when our proposal was used as a localization system, the results showed that for the same case of 1 meter, the estimation of orchard parameters improved in 20 % overall. However, in lower positioning errors of the GNSS, the estimation of orchard parameters were improved up to 50% overall. These results suggest that our work could lead to better decisions in agricultural operations, which are based on foliar parameter measurements, without the use of external hardware.
Human–robot interaction (HRI) is an extensive and diverse research topic that has been gaining importance in last years. Different fields of study have used HRI approaches for solving complicated ...problems, where humans and robots interact in some way to obtain advantages from their collaboration. Many industrial areas benefit by applying HRI strategies in their applications, and agriculture is one of the most challenging of them. Currently, field crops can reach highly autonomous levels whereas speciality crops do not. In particular, crops such as fruits and vegetables are still harvested manually, and also some tasks such as pruning and thinning have long been considered to be too complex to automate completely. In addition, several countries face the problem of farm labour shortages. As a consequence, the production process is affected. In this context, we survey HRI approaches and ap-plications focused on improving the working conditions, agility, efficiency, safety, productivity and profitability of agricultural processes, in cases where manual labour cannot be replaced by but can be complemented with robots.
•Nowadays, robotics as machinery for planting or harvesting is solved in different countries.•However, harvest of crops like fruits and vegetables is still manually handled.•Several countries face the problem of lack of human labour force.•We survey the HRI perspectives and applications focused on improving working conditions, agility, efficiency, safety.•The focus is put in cases where human labour force cannot be replaced but complemented with robots.
•Developed a modular and distributed sensing system for assisting the GPS navigation.•Designed, developed, and validated multi-channel IR sensors with CANBUS communication.•Designed a fuzzy ...knowledge-based controller, and validated it in simulation and actual field.•Tested the functionality of the proposed solution under harsh field conditions.
Autonomous navigation of mobile robots inside unstructured agricultural fields proposes serious challenges due to the extreme variations in high-density bushes, the presence of random obstacles, and the inaccuracies in the GPS and IMU measurements. Advanced perception solutions are therefore required to assist the existing GPS-based navigation and to improve the reliability of the operation. This paper reports on the development and evaluation of a modular and scalable sensing system to assist the autonomous navigation of an agricultural mobile robot by providing it with collision avoidance capabilities. The robot benefited from a four-wheel steering mechanism that could be driven remotely via a 2.4 GHz wireless transmitter and could be programmed using the Robot Operating System (ROS) to follow waypoints. Multiple arrays of Time-of-Flight and infrared sensors with independent processing units were installed on the left, right, and front of the robot to enable a distributed control system. Communication between the sensor modules was realized via a CAN network. The collision avoidance system then exchanged messages with the robot computer over Ethernet using ROS on multiple machines scheme. A virtual model of the robot with an exact sensing setup was replicated in a robotic simulator to accelerate experimenting with different control algorithms and to optimize the sensors’ functionality. The simulation scenes and dynamic models were then improved by manually driving the robot in a real berry field for collecting sensor and steering data. Results from the simulation showed that the robot was able to autonomously navigate in different tracks and stabilize itself in the presence of random obstacles using a fuzzy knowledge-based algorithm. Preliminary field tests suggested that the Exponential filter was necessary to be implemented on each sensor for removing noise and outliers. The proposed approach created a flexible framework for exchanging data between each of the sensor ECUs and preventing the robot from colliding with random obstacles in front, left, and right. The study confirmed the functionality of the affordable sensing system and control architecture and can be suggested as an alternative solution for the high-end 3D LiDARs and the complex simultaneous localization and mapping methods.
Nowadays, physically impaired people still struggle with daily tasks when using mobility aid devices, whether for crossing doors, parking or manoeuvring in their homes. In this context, assistive ...robotics can offer solutions to those problems, thus increasing the users’ quality of life. However, studies must be performed to determine the best architecture for human–robot interaction. In this work, we propose a collaborative navigation strategy for improving users’ skills for driving assistive vehicles. We present four navigation modes: manual, assisted manual, autonomous and assisted autonomous. In particular in the two assisted modes, the system is able to predict the user’s motion intentions, reducing his/her workload. The system was validated in a real world environment with a population of twenty volunteers. Objective and subjective metrics were used to asses the system’s performance and usability, with special consideration to human factors. Results show that the system aids users to perform navigation tasks in a clear and compliant manner using a robotic assistive vehicle, while decreasing their perceived workload by 15% for the assisted manual, 41% for the autonomous and 40% for the assisted autonomous, when compared to the manual mode. Additionally, it is shown that if autonomous navigation sets a lower bound for user workload, the system approximates this bound while improving performance.
•Treetop volume information of a grove is important for agricultural processes.•LiDAR scanning provides 3D information of the grove with very low error.•An optimized convex hull algorithm is used to ...estimate treetop volume based on the LiDAR readings.•The LiDAR scanning and the convex hull algorithm are sensitive to localization errors of the sensor.•A SLAM algorithm is implemented to minimize localization errors and to improve treetop volume estimation.
Treetops volume information in groves is a key component of the perception process for improving herbicide management, foliage density observation and a grove’s canopy maturity supervision. In this work, a computational geometry based approach for estimation of the treetops of a grove is implemented and tested. This approach is based on convex hull techniques to estimate the volume of a treetop from 3D raw laser data. The method shown here optimizes both the computational cost associated with the convex hull processing and the volume of stored information, which become crucial for in-field experimentation. Additionally, this work presents an analysis of how the localization of the range sensor used for treetop volume estimation, directly affects the information regarding such treetop. Thus, a mathematical and empirical analysis of treetop volume estimation using a GPS antenna and a SLAM (Simultaneous Localization and Mapping) algorithm is included, showing that the SLAM algorithm provides with a better estimation. The mathematical foundation of the proposal, as well as convergency tests and real-time experimentation results are also shown in this work.
The thermal behaviour of an orchard is intrinsically related to the plant physiological status and it is commonly observed using thermal imagery, in most cases, provided by a drone or by a satellite. ...Such remote sensing methods are currently popular since they allow to analyse large amounts of land data with few sensor readings. However, they are restricted by the spatial resolution of the images, which always correspond to top views of the canopies. The latter does not allow for a side recording or analysis of the orchard. In this work, we design and evaluate a portable ground-based system for a manual thermal and geometrical characterisation of an orchard, merging thermal images with LiDAR-based range readings in order to obtain a 3D thermal reconstruction of the crop to overcome the previously mentioned issues. The proposed system can work in Global Navigation Satellite System (GNSS) denied environments and delivers multiple views of the orchard, offering the user a three-dimensional view of the thermal behaviour of the grove. Further, the implemented algorithm classifies points from the LiDAR measurements which correspond to the canopy using a supervised classifier. Later, a matching procedure is performed between such points and the thermal information provided by the thermal camera. In order to reconstruct the entire orchard or only a section of the grove, several frames are registered using the Iterative Closest Point algorithm. The system was tested in two conditions: in laboratory and in field within a plantation of Hass avocado, which is one of the main fruit trees growing in Chile, and its performance is compared with an LI-6400 Infra-red Gas Analyser (IRGA) portable photosynthesis system (LI-COR, Lincoln, NE).
•Orchard characterisation is dependent on the nature of the grove.•Thermal and 3D characterisation require sensor integration.•Satellite imagery offers top views of the grove with low spatial resolution to the farmer.•Drones are GNSS dependent and in the slope of the Andes mountains GNSS signal is lost.•Ground-based system for characterisation of avocado groves is evaluated in the field.
•Characterization of orchards enhances agricultural processes and resource management.•Four computational geometry methods to estimate tree canopy volumes were evaluated.•The methodologies were ...validated using real agricultural scenarios 3D LiDAR data.•The methodologies have shown to converge to steady state estimations of the volume.•Resources can be saved when partially scanning canopies.
Efficient information management in orchard characterization leads to more efficient agricultural processes. In this brief, a set of computational geometry methods are presented and evaluated for orchard characterization; in particular, for the estimation of canopy volume and shape in groves and orchards using a LiDAR (Light Detection And Ranging) sensor mounted on an agricultural service unit. The proposed approaches were evaluated and validated in the field, showing they are convergent in the estimation process and that they are able to estimate the crown volume for fully scanned canopies in real time; for partially observed tree crowns, accuracy decreases up to 30% (the worst case). The latter is the major contribution of this brief since it implies that the automated service unit does not need to cover all alley-ways for an accurate modeling of the orchard, thus saving valuable resources.
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.
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