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A removal system for Unmanned Ground Vehicles (UGVs) equipped with a modular robotic arm designed for hazardous and unstructured environments is presented. The system is based on commercially available solution that enables the manipulation of objects and removal of obstacles in complex terrains, such as disaster zones, battlefields, and industrial sites. The operational environment, teleoperation capabilities, and associated challenges are thoroughly addressed. Recent developments in All Terrain Vehicle-based unmanned platforms are discussed, demonstrating their suitability for rapid and versatile implementation in special-purpose missions. A model of an unmanned platform with an integrated robotic arm is proposed to enhance the existing range of medium-sized UGVs. The system configuration tailored for special-purpose missions is outlined, and kinematic parameters, along with stress tests, are provided to confirm its high adaptability to a diverse range of operational scenarios. [ABSTRACT FROM AUTHOR]

First-person view (FPV) technology in virtual reality (VR) can offer in-situ environments in which teleoperators can manipulate unmanned ground vehicles (UGVs). However, non-experts and expert robot teleoperators still have trouble controlling robots remotely in various situations. For example, obstacles are not easy to avoid when teleoperating UGVs in dim, dangerous, and difficult-to-access areas with environmental obstacles, while unstable lighting can cause teleoperators to feel stressed. To support teleoperators' ability to operate UGVs efficiently, we adopted construction yellow and black lines from our everyday life as a standard design space and customised the Sobel algorithm to develop VR-mediated teleoperations to enhance teleoperators' performance. Our results show that our approach can improve user performance on avoidance tasks involving static and dynamic obstacles and reduce workload demands and simulator sickness. Our results also demonstrate that with other adjustment combinations (e.g., removing the original image from edge-enhanced images with a blue filter and yellow edges), we can reduce the effect of high-exposure performance in a dark environment on operation accuracy. Our present work can serve as a solid case for using VR to mediate and enhance teleoperation operations with a wider range of applications. [ABSTRACT FROM AUTHOR]

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University's Center for Advanced Vehicular Systems. [ABSTRACT FROM AUTHOR]

This article explores the use of unmanned systems for evacuating wounded personnel from the battlefield. It focuses on conclusions drawn from conventional medical evacuation methods, particularly when wounded soldiers are still in the enemy’s operational zone, following the principles of Tactical Combat Casualty Care (TCCC). Then, general conditions of the injured were determined, which would require evacuation to a safe zone. This allowed us to define the critical capabilities needed for unmanned systems to perform these tasks. The ability of the unmanned system to pick up an injured person who is unconscious and unable to cooperate was identified as crucial. In the further part of the work, existing unmanned systems intended for rescue tasks were collected and critically analysed.

The study analyses challenges faced by robots used by EOD subunits, their working conditions, and possible damage during operations. The literature review takes into account long-term and incidental loads on the undercarriage and drive systems, which affect the functionality and durability of these robots. Moreover, the limitations and advantages of additive technology in restoring the functionality of these machines were verified, taking into account the operational environment of EOD subunits.

Collaborative encirclement of multiple unmanned ground vehicles (UGVs) is a focal challenge in the realm of multiagent collaborative tasks, representing a fundamental issue in complex undertakings such as multiagent collaborative search and interception. Although optimization algorithms have yielded rich research outcomes in collaborative encirclement, challenges persist, including poor real-time computational efficiency and weak robustness. Reinforcement learning theory holds considerable promise for addressing multiagent sequential decision problems. This paper delves into the study of the collaborative encirclement of multiple UGVs based on deep reinforcement learning theory, focusing on the following key aspects: establishing a kinematic model for UGVs to describe the collaborative encirclement task, detailing the collaborative encirclement process, developing strategies for target UGV escape, and addressing challenges arising from the increasing number of UGVs, which results in a complex environment and issues such as algorithmic instability, dimension explosion, and poor convergence. This paper introduces a collaborative encirclement algorithm based on the soft actor–critic (SAC) framework. To address issues related to poor collaboration and weak generalization among multiple UGVs, long short-term memory is incorporated into the network structure, serving as a memory function for UGVs. This tactic aids in capturing and using information from historical observation sequences, effectively processing time–series data, making more accurate decisions, promoting mutual collaboration among UGVs, and enhancing system stability. To tackle the issue of increased state space dimensions and low training efficiency during collaborative encirclement, an attention mechanism is introduced to calculate and select attention weights in the state space, focusing attention on key states relevant to the task. This strategy helps constrain state space dimensions, ensuring network stability, achieving stable and efficient collaboration among multiple UGVs, and improving algorithm training efficiency. To address the problem of sparse rewards in collaborative encirclement tasks, a mixed reward function is proposed that divides the reward function into individual and collaborative rewards. Individual rewards guide UGVs toward the target, incentivizing their motion behavior, whereas collaborative rewards motivate a group of UGVs to collectively accomplish the encirclement task. This approach further guides UGVs to obtain more frequent reward signals, ultimately enhancing the algorithm convergence speed. Simulation and experimental results demonstrate that the proposed method achieves faster convergence than SAC, with a 15.1% reduction in encirclement time and a 7.6% improvement in success rate. Finally, the improved algorithm developed in this paper is deployed on a UGV platform, and real-world experiments in typical encirclement scenarios validate its feasibility and effectiveness in embedded systems.

The article substantiates the rational design of the crank mechanism of a wheel-step mover. Kinematics analysis of a prototype of the wheel-step mover revealed the presence of oscillations in the vertical coordinate of the central axis during movement. To eliminate this drawback, differential geometry methods were used, thanks to which a method for calculating the non-circular profile of support shoes was created. Studies of the dynamics of the crank mechanism of the wheel-step mover showed that even in a steady state of motion, periodically acting inertial forces arise. To reduce their effect, non-circular gears should be used in the mover power drive. The dimensions of the crank mechanism links have been calculated, at which the cross-country ability of the running system, its kinematics and dynamics characteristics are improved, energy costs when laying a track are reduced, as well as the negative impact of the machine on the fertile soil layer. The polar coordinates of the involute points have been calculated. They are needed to construct the tooth profiles of non-circular gears. As the power drive of the crank mechanism, non-circular gears have symmetrical geometric parameters along two axes and provide a constant movement speed. Thus, the wheel-step running system can be used in agriculture, the timber industry, mining, and when working in emergency situations. The mover design being developed is intended for unmanned ground vehicles. This type of unmanned vehicles is at the initial stage of its development, inferior in frequency to air and marine unmanned vehicles. A targeted concentration of production and financial resources is necessary to ensure that domestic industry has a leading position in the production and sale of unmanned ground vehicles.

To support people working in dangerous industries, virtual reality (VR) can ensure operators manipulate standardized tasks and work collaboratively to deal with potential risks. Surprisingly, limited research has paid attention to the cognitive load of operators in their collaborative tasks, especially via VR interfaces. Once task demands become complex, many researchers focus on optimizing the design of the interaction interfaces to reduce the cognitive load on the operator. In this paper, we propose a new collaborative VR system with edge enhancement to support two teleoperators working in the VR environment to remote control an uncrewed ground vehicle. We use a compared experiment to evaluate the collaborative VR systems, focusing on the time spent on tasks and the total number of operations. Our results show that the total number of processes and the cognitive load during operations were significantly lower in the two-person group than in the single-person group. Our study sheds light on designing VR systems to support collaborative work with respect to the flow of work of teleoperators instead of simply optimizing the design outcomes. [ABSTRACT FROM AUTHOR]

Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration. [ABSTRACT FROM AUTHOR]

The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity. [ABSTRACT FROM AUTHOR]

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

Facing the dual challenges of limited natural resources, particularly water, and increasing demands for food security, the role of precision agriculture has become increasingly critical, especially in resource-constrained environments of developing countries. Since its inception in the mid-1980s, precision agriculture in the United States and Europe has evolved to integrate advanced technologies to optimize resource use and minimize environmental impacts, yet it remains in its nascent stages in major developing countries despite attempts at precision irrigation practices. This paper reviews the evolution of soil moisture monitoring technologies from 2013 to 2024, analyzing their strengths, weaknesses, and accuracy in various soil depths to guide modern practices in water and irrigation management within precision agriculture. Highlighting the shift from traditional methods to innovative on-the-go sensors, including electrical, optical, and radiometric sensors, as well as the deployment of unmanned aerial vehicles (UAVs), satellite remote sensing (RS), and IoT-based systems and wireless sensor networks (WSNs), these technologies provide timely, cost-effective, and detailed soil moisture data, significantly enhancing irrigation precision. Our analysis not only showcases technological advancements but also identifies current research gaps, proposing the integration of these technologies to enhance soil moisture management and support sustainable, high-yield agriculture. The anticipated benefits of this integration include improved soil health, increased crop yields, and more effective utilization of water resources, crucial for the sustainability of agriculture in developing nations. By bridging the research-practice gap, this paper aims to foster the adoption of these technologies in precision agriculture, addressing specific agricultural challenges in water-limited settings.

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems.

Unmanned ground vehicles (UGVs) have emerged as a promising solution for reconnaissance missions, overcoming labor cost, frequency, and subjectivity issues associated with manual procedures. However, for dynamic environments such as construction sites, the constantly changing conditions hinder a manager from planning the UGV's paths. For an autonomous monitoring mission, the path planning should be dealt with by having a map with the site's most recent scene. In this study, we develop a mobile app capable of local map updates by overlaying an image on a mapping API (e.g., Google Maps) hence working as a digital twin capable of creating a dynamic representation of the updated terrain over the mapping API. UGV operators can draw a path on such an updated construction scene using a tablet PC or smartphone. Discrete GPS information (e.g., latitudinal, and longitudinal) is executed for the UGV's controller. In the overlaying procedure, the homographic relation between the image and map is automatically computed and then projected. We successfully demonstrated the capabilities of the technique with two construction sites and a soccer field using images from the ground and satellite, respectively. The error generation is quantitatively measured and analyzed. [ABSTRACT FROM AUTHOR]

Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration.

Abstract The increasing potential of unmanned systems promises massive changes in military tactics and enormous enhancements to army arsenals. However, these developments also raise many concerns for societies as the use of force could potentially be delegated to autonomous weapons systems (AWS) that could operate without meaningful involvement of the military chain of command. This paper [Article adapts from a collaborative research report of Estonian research group nr R-012 on the iMUGS (Integrated Modular Unmanned Ground System) project, funded by the EDIDP (European Defence Industrial Development Programme).] reports the findings of interviews with military personnel (n = 18), which were conducted to gauge their perceptions and attitudes towards UGVs and the transformation to autonomous weapons. The findings indicate that the patriotic mentality to fight for freedom is perceived as inconceivable for an AI because unmanned platforms can hardly ever be equipped with patriotical algorithms. However, participants agreed that AI-driven machines should be perceived as decision-making instruments at best, with the caveat that any discretion to use lethal force should not be delegated to the machine.

3 J. Yang and S. Lee, Ultra-wideband coupled relative positioning algorithm applicable to flight controller for multidrone collaboration, ETRI J. 45 (2023), no. 5. In a UAV-assisted surveillance system, UAVs are equipped with learning-based object recognition models and can collect surveillance images. Keywords: drones; UAV; unmanned autonomous vehicles; unmanned ground vehicles EN drones UAV unmanned autonomous vehicles unmanned ground vehicles 731 734 4 10/31/23 20231001 NES 231001 Recently, research on autonomous mobility control has been actively and widely conducted for various applications. [Extracted from the article]

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains.

Evaluating the risk associated with operations is an essential element of safe planning and an essential prerequisite in mobile robotics. This issue is very broad, with numerous definitions emerging in the recent literature adapting different application scenarios and leading to different algorithmic approaches. In this review, we will investigate how the state-of-the-art approaches define the traversability risk, particularly for mobile robots, whereby we classify existing risk-aware navigation algorithms according to their characterization of risk. Subsequently, we will overview the formulations of risk assessment along a path using traversability grid maps since it is essential for a mobile robot to evaluate its path to predict potential hazards. Finally, we will discuss the consistency of commonly used risk metrics in robotics. The aim of the review is to offer a comprehensive overview to newcomers in the field, to provide a structured reference for practitioners, and to also inspire future developments.

Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor. [ABSTRACT FROM AUTHOR]

The increasing potential of unmanned systems promises massive changes in military tactics and enormous enhancements to army arsenals. However, these developments also raise many concerns for societies as the use of force could potentially be delegated to autonomous weapons systems (AWS) that could operate without meaningful involvement of the military chain of command. This paper [Article adapts from a collaborative research report of Estonian research group nr R-012 on the iMUGS (Integrated Modular Unmanned Ground System) project, funded by the EDIDP (European Defence Industrial Development Programme).] reports the findings of interviews with military personnel (n = 18), which were conducted to gauge their perceptions and attitudes towards UGVs and the transformation to autonomous weapons. The findings indicate that the patriotic mentality to fight for freedom is perceived as inconceivable for an AI because unmanned platforms can hardly ever be equipped with patriotical algorithms. However, participants agreed that AI-driven machines should be perceived as decision-making instruments at best, with the caveat that any discretion to use lethal force should not be delegated to the machine. [ABSTRACT FROM AUTHOR]

Nestled at the confluence of nature grandeur and human civilization, beaches command an influential presence that resonates throughout the environment, society, and culture. However, climate change and pollution overhang the beach health and need to be properly dealt with. Proactive measures involve education, responsible waste management, sustainable infrastructure, and environmental regulations, while reactive ones focus on immediate response and cleanup efforts. Nevertheless, continuous monitoring and cleaning are challenging due to various factors such as beach characteristics, hidden waste, weather conditions and, consequently, high costs. To overcome such challenges, this paper proposes an autonomous system for beach cleaning adopting an Intelligent Hierarchical Cyber-Physical System (IHCPS) approach and Information and Communication Technologies. The proposed beach waste management (BeWastMan) solution integrates an Unmanned Aerial Vehicle for the beach aerial surveillance and monitoring, a ground station for data processing, and an Unmanned Ground Vehicle to collect and sort waste autonomously. The research findings contribute to the development of innovative and fully automated approaches in beach waste management, and demonstrate the feasibility and effectiveness of the BeWastMan IHCPS by a real case study, developed in the frame of the BIOBLU project.

The Inertial Navigation System/Global Positioning System (INS/GPS) fusion navigation technology commonly used by unmanned ground vehicles is prone to GPS interruption in complex environments, resulting in the inability of the system to locate accurately. This paper designs an INS/GPS/Odometer/Vision/Magnetometer fusion navigation scheme in response to this problem. Add odometer, vision, and magnetometer to the INS/GPS integrated navigation system, use odometer dead reckoning to suppress the position offset caused by GPS interruption, reduce the cumulative error of the system through visual positioning, and correct the course of INS with the help of magnetometer orientation Angle, improve the positioning ability of the system in the GPS interruption environment. In order to further enhance the positioning accuracy of the system, the Federal Filter algorithm (FKF) was improved, and the Federal Robust Cubature Kalman Filter algorithm (FRCKF) was proposed. A Robust Cubature Kalman Filter algorithm is introduced into the framework of the federated filter algorithm, which improves the processing ability of the FKF algorithm for nonlinear systems and reduces the positioning error of the system. Finally, an experimental platform for unmanned ground vehicle navigation was developed, and the effectiveness of the multi-source fusion navigation system was verified through vehicle navigation experiments. The experiment results show that the multi-source fusion navigation system designed in this paper can effectively reduce the negative impact of GPS interruption on the INS/GPS integrated navigation system and improve the positioning accuracy of unmanned ground vehicles.

This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There are many different applications of mobile platforms in both civilian and military fields. Herein, we introduce a newly developed manned–unmanned high-mobility vehicle called TAERO that was designed for public roads and off-road operation. Detection for unmanned mode is required in both on-road and off-road environments, but the approach to identify drivable pathway and obstacles around a mobile platform is different in each environment. Dense vegetation and trees can affect the perception system of the vehicle, causing safety risks or even collisions. The main aim was to define the limitations of the perception system in off-road environments, as well as associated challenges and possible future directions for practical applications, to improve the performance of the UGV in all-terrain conditions. Recorded datasets were used to verify vision and laser-based sensors in practical application. The future directions of work to overcome or minimize the indicated challenges are also discussed. [ABSTRACT FROM AUTHOR]

This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV. [ABSTRACT FROM AUTHOR]

Throwable Unmanned Ground Vehicles are light weight, small size, easily deployable and impact resistive vehicles mainly used in domestic as well as in military purposes where human life is compromised such as inspection of sewage pipes, and search and rescue operations etc. Main challenge while considering a Throwable UGV is its impact absorption capability which is seen through its material properties. Wheels of Throwable UGV absorb most of the impact without harming the internal structure. To solve this problem a honeycomb structured wheel is designed and simulated in ANSYS Workbench. Vulcanized rubber and Plastic composite PCTPE material wheels were impact tested using explicit dynamic analysis tool in ANSYS workbench. Total deformation, equivalent stress and strain results are tested in ANSYS under impact testing of wheel which is dropped from 10 meter height with velocity of 14m/s in a concrete surface. A plastic composite material PCTPE was 3D-printed and was used in Throwable Unmanned Ground Vehicle.

Unmanned ground vehicles (UGVs) find extensive use in various applications, including that within industrial environments. Efforts have been made to develop cheap, portable, and light-ranging/positioning systems to accurately locate their absolute/relative position and to automatically avoid potential obstacles and/or collisions with other drones. To this aim, a promising solution is the use of ultrasonic systems, which can be set up on UGVs and can potentially output a precise reconstruction of the drone's surroundings. In this framework, a so-called frequency-modulated continuous wave (FMCW) scheme is widely employed as a distance estimator. However, this technique suffers from low repeatability and accuracy at ranges of less than 50 mm when used in combination with low-resource hardware and commercial narrowband transducers, which is a distance range of the utmost importance to avoid potential collisions and/or imaging UGV surroundings. We hereby propose a modified FMCW-based scheme using an ad hoc time-shift of the reference signal. This was shown to improve performance at ranges below 50 mm while leaving the signal unaltered at greater distances. The capabilities of the modified FMCW were evaluated numerically and experimentally. A dramatic enhancement in performance was found for the proposed FMCW with respect to its standard counterpart, which is very close to that of the correlation approach. This work paves the way for the future use of FMCWs in applications requiring high precision. [ABSTRACT FROM AUTHOR]

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths when navigating on unknown terrains.

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases. • Autonomous ground and aerial vehicles can lead to improve agriculture management. • A collaborative scheme based on a multi-phase approach is proposed. • A specific case study involving a vineyard located in the Barolo is presented. • Design of advanced GNC schemes are proposed for autonomous navigation of drones. [ABSTRACT FROM AUTHOR]

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here. • Autonomous ground and aerial vehicles can lead to improve agriculture management. • A collaborative scheme based on a multi-phase approach is proposed. • Analysis of the technological and methodological challenges is presented. • Companion paper reports an application in a vineyard case study. [ABSTRACT FROM AUTHOR]

Traditional formation control methods are widely used in the field of unmanned ground vehicle formation, but they lack mechanisms with which to effectively cope with complex terrains that occur during movement. In order to better improve the adaptation and coping ability of an unmanned ground vehicles (UGVs) fleet to complex terrains, this paper proposes a formation change influence factor to solve the UGVs formation and formation change problem. First, this paper adopts the leader–follower method with more flexible control to design the formation controller and derives a control law that can make the formation system stable so as to ensure that the fleet maintains the preset formation during movement. After that, this paper combines formation geometry change and dynamic adjustment to build a formation change library. The formation change influence factor is used to drive the fleet to choose the appropriate formation change strategy in the formation change library to ensure the fleet can safely pass the complex terrains. The experimental results show that, compared with the traditional formation method, the UGVs formation and change method using the formation change influence factor can flexibly and efficiently cope with various complex terrains while maintaining stability within the fleet, effectively improving the safety of the UGVs fleet and the possibility of practical application. [ABSTRACT FROM AUTHOR]

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Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor.

This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There are many different applications of mobile platforms in both civilian and military fields. Herein, we introduce a newly developed manned–unmanned high-mobility vehicle called TAERO that was designed for public roads and off-road operation. Detection for unmanned mode is required in both on-road and off-road environments, but the approach to identify drivable pathway and obstacles around a mobile platform is different in each environment. Dense vegetation and trees can affect the perception system of the vehicle, causing safety risks or even collisions. The main aim was to define the limitations of the perception system in off-road environments, as well as associated challenges and possible future directions for practical applications, to improve the performance of the UGV in all-terrain conditions. Recorded datasets were used to verify vision and laser-based sensors in practical application. The future directions of work to overcome or minimize the indicated challenges are also discussed.

This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV.

We present simulation scenarios of private and public transportation modes of transportation being replaced by autonomous mobility-on-demand (AMoD) system in Edinburgh, UK, based on realistic temporal and spatial demand patterns. Edinburgh is interesting to simulate because it is selected to be one of the top 50 congested cities despite the traditional layout of the city with a low population density. For each mode of transportation, we test five different operational policies (from the literature) for different fleet sizes. We examine the results both in terms of system expenditures and profits, as well as service quality. Additionally, we evaluate Pareto solutions that combine policies' performance to give a trade-off between wait times and empty miles. In comparison with private cars, the Autonomous Vehicles (AV) resulted in 86% reduction in the number of cars on the road. In addition, the use of the AVs per day is at least two to seven times higher than the UK private car usage. A reduction in the number of vehicles will have a positive influence on the congestion. When compared with the public mode, AMoD vehicles require 62% less time and 60% less distance than buses for the same trips. [ABSTRACT FROM AUTHOR]