Search

Unmanned Ground Vehicles (UGV) are devices capable of performing basic working movements without the operator being in their immediate working environment. Their capabilities include but are not limited to the perception of the environment with the use of sensors, determining the platform’s position, and planning and executing its movement. Ultra Wideband (UWB) is one of the wireless communication technologies which is increasingly used in location systems. This article presents the use of UWB technology in developing a guide localization system for a UGV (one of the stages of implementing a follow-me system). The article describes tests carried out on the developed testbed. Their aim was to determine the hardware configuration of the anchor arrangement characterized by the minimum number of lost data packets during operation. In order to determine the influence of the analysed variables on the output values, the method of global sensitivity analysis for neural networks was used.

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.

Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an end-to-end and completely autonomous global path planner., Comment: Submitted to Computers and Electronics in Agriculture

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Autonomous ground and aerial vehicle can lead to favorable improvements in management by performing in-field tasks in a time-effective way. Greater benefits can be achieved by allowing cooperation and collaborative action among Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). A multi-phase approach is here proposed, where each unmanned vehicle involved has been conceived and will be designed to implement innovative solutions for automated navigation and infield operations within a complex irregular and unstructured scenario as vineyards in sloped terrains.

This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies investigating agricultural robotic systems with regard to: (i) the analysis approach, i.e., simulation, emulation, real-world implementation, (ii) farming operations, and (iii) the farming type. Our analysis demonstrates that simulation and emulation modelling have been extensively applied to study advanced agricultural machinery while the majority of the extant research efforts focuses on harvesting/picking/mowing and fertilizing/spraying activities, most studies consider a generic agricultural layout. Thereafter, we developed AgROS, an emulation tool based on the Robot Operating System, which could be used for assessing the efficiency of real-world robot systems in customized fields. The AgROS allows farmers to select their actual field from a map layout, import the landscape of the field, add characteristics of the actual agricultural layout (e.g., trees, static objects), select an agricultural robot from a predefined list of commercial systems, import the selected UGV into the emulation environment, and test the robot&rsquo, s performance in a quasi-real-world environment. AgROS supports farmers in the ex-ante analysis and performance evaluation of robotized precision farming operations while lays the foundations for realizing &ldquo, digital twins&rdquo, in agriculture.

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

Ultra-wideband (UWB) technology is one of the most promising wireless communication technologies. Examples of UWB applications include, among others, radiocommunication devices and location systems, due to their operating range, ability to work in outdoor environments, and resistance to multipath effects. This article focuses on the use of UWB technology in constructing a guide localization system for an unmanned ground vehicle (UGV), which is one of the stages of implementing a &ldquo, follow me&rdquo, system. This article describes the complete process of UWB signal processing from its acquisition, methods of filtering, and obtained results, to determining the location of the guide. This article examines the possibility of using modified versions of localization algorithms for determining the guide&rsquo, s location, including trilateration, methods of nonlinear programming, and a geometric algorithm proposed by us. The innovation of this study consists in the implementation of an algorithm that changes the selection of equations (mathematical model) for determining location based on the number of available measurements from UWB sensors.

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time- and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR , is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal.

[Resumen] El despliegue de plataformas robóticas como apoyo a la gestión de situaciones de emergencia constituye un reto que persigue mejorar la eficiencia en misiones de búsqueda y rescate. Este artículo propone un planificador estratégico para el rescate de víctimas mediante un equipo de robots terrestres. Este sistema inteligente permitirá planificar las trayectorias para acceder a las víctimas de acuerdo con su ubicación y prioridad de asistencia médica (triaje). Para la planificación se propone una nueva variante del algoritmo “fast marching method” (FMM). En este trabajo se ofrecen resultados preliminares mediante un ejemplo de aplicación en condiciones simuladas. [Abstract] The deployment of robotic platforms in support of emergency management is a challange that seeks to improve efficiency in search and rescue missions. This paper proposes a strategic planner for the rescue of victims with a team of ground robots. The purpose of this intelligent system is planning the routes to access a number of victims according to their location and priority of medical assistance (i.e., triage). In particular, this paper offers work in progress regarding a new anisotropic variant of the fast marching method (FMM). Furthermore, preliminary results illustrate the application under simulated conditions. Ministerio de Economía, Industria y Competitividad; DPI2015-65186-R Ministerio de Economía, Industria y Competitividad; BES-2016-077022

SUMMARYReliable assessment of terrain traversability using multi-sensory input is a key issue for driving automation, particularly when the domain is unstructured or semi-structured, as in natural environments. In this paper, LIDAR-stereo combination is proposed to detect traversable ground in outdoor applications. The system integrates two self-learning classifiers, one based on LIDAR data and one based on stereo data, to detect the broad class of drivable ground. Each single-sensor classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the classifier automatically learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers are statistically combined in order to exploit their individual strengths and reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in rural environments, are presented to validate and assess the performance of this approach, showing its effectiveness and potential applicability to autonomous navigation in outdoor contexts.

The paper addresses the localization issue for skid-steer vehicles. The large extent of slippage incurred by this type of vehicle in a turn heavily degrades the accuracy of their position estimation systems. Based on the kinematics modelling of skid-steering, the concept of equivalent track is presented. During field experiments with an off-road unmanned vehicle, this new concept proved to be effective in reducing localization errors up to two orders of magnitude.

In this paper we propose a framework for trajectory planning and control of tracked vehicles for rescue environments, based on Augmented Reality (AR). The framework provides the human operator with an AR-based interface that facilitates both 3D path planning and obstacle negotiation. The interface converts the 3D movements of a marker pen, handheld by the operator, into trajectories feasible for the tracked vehicle. The framework implements a trajectory tracking controller to allow the tracked vehicle to autonomously follow the trajectories, decided by the operator. This controller relies on a localization system which provides, at real-time, position feedback. The localization system exploits the performance of a Dead Reckoning System together with the accuracy of an ICP-based SLAM in pose estimation, to determine the pose of the tracked vehicle within the 3D map. We demonstrate the application of the planning framework in autonomous robot navigation for evaluating the robot capabilities in rescue environments. Our experiments show the effectiveness of the trajectory tracking control method.

Ground robotic vehicles are continuing to improve in intelligence, mobility, and reliability. Today, more than 3000 ground robotic vehicles are being used by the U. S. Army in the field. These vehicles' duties range from vehicle security to IED detection and neutralization. However, the current operation of ground robotic vehicles are remote control and tele-operational. Underdeveloped adaptive and contextual reasoning algorithms and testing methodologies are limiting their abilities to operate more autonomously. An evaluation framework and a set of metrics need to be developed to enable the research results and value in algorithms to be assessed. In this paper, a simple measure of collaboration between Unmanned Ground Vehicles (UGV) is introduced. The measure is designed to be simple enough to test most all UGVs against. Case studies are used as examples. Refereed/Peer-reviewed