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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]

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]

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]

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]

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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Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches. [ABSTRACT FROM AUTHOR]

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time. [ABSTRACT FROM AUTHOR]

Unmanned ground vehicles (UGVs) have been utilized in many civilian fields in addition to the traditional military field. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. Many applications require the UGV to autonomously navigate static environments while taking into consideration obstacle avoidance. Autonomous path planning is one of the key challenges and issues related to UGVs. Generally, robotic path planning is an optimization search problem that comes in different forms. Some of its forms have been solved by different classical algorithms such as A*, but these algorithms are computationally inefficient. In contrast, the emerging nature-inspired algorithms outperform the classical ones since the computational overhead is reduced. Nature-inspired algorithms are among the most common heuristic algorithms. This paper proposes a near-optimal algorithm to find a feasible path for UGV in a static environment. The performance of the proposed algorithm was compared with other well-established algorithms in path planning literature such as A* using a simulator developed for this purpose. The simulator tests three performance measures path length, farness from obstacles, and running time. The simulator results revealed that the length of the path generated by this algorithm is near-optimal, however, the generated path is kept far from obstacles. [ABSTRACT FROM AUTHOR]

Highlights • Focus on trade-off between mobility and latency in unmanned ground vehicles (UGVs). • Focus on a path following task. • Novel empirical trade-off relationships for large, high speed, teleoperated UGVs. • A benchmark for the U.S. Army's evaluations of future UGV technologies. Abstract As the penetration levels of unmanned ground vehicles (UGVs) in military applications increase, there is a growing need to evaluate their mobility across different latencies and various modes of operation ranging from pure teleoperation to full autonomy. State-of-the-art tools to evaluate mobility of ground vehicles do not address this need due to their not accounting for UGV technologies and the associated latencies. Although the trade-off between latency and performance has been thoroughly studied in the telerobotics literature and the results may qualitatively shed light onto the UGV domain, as well, a quantitative generalization is not possible due to the differences in context. Recognizing this gap, this paper presents a functional relationship between mobility and latency in high-speed, teleoperated UGVs under the context of path following. Specifically, data from human-in-the-loop simulations performed in this paper are combined with data from prior studies to span three vehicle types, three courses, and teleoperation latencies ranging from 0 s to 1 s. This combination yields for the first time a diverse data set for the context of path following in high speed, teleoperated UGVs. Based on this data set, empirical relationships are derived to quantify the trade-off between latency versus average speed and lane keeping error. These relationships can be used to establish a benchmark to evaluate the performance of autonomy-enabled UGV systems. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper suggests an integrated navigation system for an unmanned ground vehicle operating in an unknown cluttered environment. The navigator supports time-critical mobility making it possible for a mobile robot to reach a target from the first attempt without the need for a dedicated exploration and mapping stage. The robot only uses necessary and sufficient egocentric local sensory data collected on its way to the target. The construction of the navigation structure revolves around key properties of the harmonic potential field approach to motion planning. The robot's trajectory is well-behaved and direct-to-the-goal. It contains only the minimum number of detours necessary to accommodate the sensory data and maintain the robot in a safe, goal-oriented state. The navigation structure is developed and its theoretical basis is explained. Extensive experimental validation of its properties and performance is carried-out using the X80 robotic platform. [ABSTRACT FROM AUTHOR]

The article presents a study that examines skill development and workload among those piloting teleoperated unmanned ground vehicles (UGVs) through openings of different widths using the so-called cornering law. Two groups of novices operated UGVs using either a first-person or third-person-overhead view. In both cases the cornering law was successful in modeling how aperture width affected driving performance.

Area coverage is a common task for an unmanned ground vehicle (UGV) that requires time and energy to complete. We have developed a novel cost function that can be used to optimally traverse a path that covers a region. The UGV model and cost function are developed theoretically and verified experimentally. Our cost function weights force inputs, area covered and motor efficiency to create an optimal trajectory. This trajectory is constrained to follow a coverage path described in the literature. The path is modified based on the cost function by replacing turn-in-place maneuvers by moving turns. Tradeoffs are presented for three cases: (1) drive motor efficiency is not considered, (2) the motors are most efficient at the maximum velocity, and (3) the motors are most efficient below the maximum velocity. Optimality tradeoffs include the time required to cover the region, and the energy required to complete the trajectory. Experimental results using an iRobot Packbot are presented. [ABSTRACT FROM AUTHOR]

Abstract: Motion planning for unmanned ground vehicles (UGV) constitutes a domain of research where several disciplines meet, ranging from artificial intelligence and machine learning to robot perception and computer vision. In view of the plurality of related applications such as planetary exploration, search and rescue, agriculture, mining and off-road exploration, the aim of the present survey is to review the field of 3D terrain traversability analysis that is employed at a preceding stage as a means to effectively and efficiently guide the task of motion planning. We identify that in the epicenter of all related methodologies, 3D terrain information is used which is acquired from LIDAR, stereo range data, color or other sensory data and occasionally combined with static or dynamic vehicle models expressing the interaction of the vehicle with the terrain. By taxonomizing the various directions that have been explored in terrain perception and analysis, this review takes a step toward agglomerating the dispersed contributions from individual domains by elaborating on a number of key similarities as well as differences, in order to stimulate research in addressing the open challenges and inspire future developments. [Copyright &y& Elsevier]

Aiming at the problem of cross-country environment perception of unmanned ground vehicle, Dempster fusion rules are applied to identifying obstacle. Firstly, five kinds of representative features are selected based on CCD and laser sensor. Secondly, sensor data is transformed to evidence space, and the obstacle identification membership is computed by using fuzzy interpolative method., then correlation coefficient is obtained. Thirdly, according to obstacle identity and weight correlation, experimental formula is selected to compute basic probability assignment function. Finally, based on Dempster fusion rules, the ultimate basic probability assignment function is acquired, the identification and decision-making rules are set to determine obstacle classification. Test results show the good robustness and real-time property by using D-S theory to identify obstacle. [ABSTRACT FROM AUTHOR]

Objective: The effect of viewpoint on the navigation of complex terrain and on spatial awareness was examined with the use of a simulated remotely operated vehicle.Background: The ability to build terrain models in real time may soon allow remote vehicular control from any viewpoint. A virtual tether couples the viewpoint to the vehicle’s position and orientation, but shows more of the terrain than a fully immersive egocentric display. In this sense, it provides visual momentum by providing a view that incorporates egocentric and exocentric qualities.Method: For this study, 12 participants navigated a simulated vehicle across complex virtual terrain using five different display viewpoints: egocentric, dynamic tether, rigid tether, 3-D exocentric, and 2-D exocentric. While navigating, participants had to avoid being seen by simulated enemy units. After the navigation task, participants’ spatial awareness was assessed using a recognition task.Results: The tethered displays minimized the time during which the participant’s vehicle was visible to enemy positions. The egocentric display was more effective than exocentric displays (2-D or 3-D) for navigation, and the exocentric displays were more effective than egocentric for time seen during navigation and the recognition task. The tethered displays produced intermediate results for navigation and recognition.Conclusion: Viewpoint tethering produced the most effective displays for minimizing time seen, but tethered displays were less effective than egocentric and exocentric displays for navigation and recognition, respectively.Application: A tethered display is recommended for applications in which it is necessary to understand the relation of nearby locations to one’s own location. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of determining the maximum allowable speed (V) of a vehicle traversing unknown off-road terrains. The calculated maximum speed achieves the fastest navigation without exceeding an allowable range of transmitted force (Fall) to the vehicle's frame. The proposed system enables the vehicle to transit between different terrains safely. The system's input are: (i) a 3D range image of the terrain and (ii) the vehicle's dimensions and characteristics (e.g., suspension parameters). First the terrain roughness is assessed; then the corresponding maximum allowable speed is calculated. In this paper a novel Roughness Index (RI) is used to represent the terrain roughness. This index is calculated based on the standard deviation of the terrain points' elevations (3D range image). A closed form expression of the maximum allowable vehicle speed is developed (as function of the vehicle's properties, Fall, RI, and probability of not exceeding Fall). The proposed system can be used as a driver assistant system to enhance the vehicle performance, increase its life time, and reduce the maintenance cost. In addition, it is a key module in Unmanned Ground Vehicles (UGVs) navigation systems; as it provides the navigation system with necessary information for path and speed planning. [ABSTRACT FROM AUTHOR]

• Aiming to improve the performance of AWIA UGV in diagonal steering mode under critical circumstances, a novel integrated control approach is extended based on robust H 2 /H ∞ theory. • A wheel force distribution controller in the control executive layer interfaced to actuators is applied to achieve the resultant active control moment at each wheel corner, by controlling each independent drive motor, independent steer motor, and active suspension actuator. • The effectiveness of the proposed control approach is validated by a real AWIA UGV testbed by performing the step-steer experiments. In the future civilian Intelligent Transportation System (ITS) and military area, the Unmanned Ground Vehicles (UGVs) are supposed to replace humans to conduct various tasks in wide civilian or military applications. This paper aims at improving the overall performance of the All-wheel-independently-actuated (AWIA) UGV. Each wheel of the AWIA UGV is independently driven and steered by the motors with independent active suspension equipped to achieve the great potential of mobility, maneuverability, and controllability. Aiming to improve the performance of AWIA UGV in diagonal steering mode under critical circumstances, a novel integrated control approach is extended in this paper based on robust H 2 /H ∞ theory. The major control objective is to generate additional vehicle active moment by full X-by-wire technique to collaboratively control lateral and roll motion of the vehicle to maintain a target diagonal steering performance. A wheel force distribution controller in the control executive layer interfaced to actuators is applied to achieve the resultant active control moment at each wheel corner, by controlling each independent drive motor, independent steer motor, and active suspension actuator. The effectiveness of the proposed control approach is validated by an AWIA UGV testbed by performing the step-steer experiments. [ABSTRACT FROM AUTHOR]

In this study, autonomous navigation of an Unmanned Ground Vehicle moving in an unknown environment is realized with Genetic Algorithms using Sectoral Grid Based Axising Method. For the algorithm, three dimensional environment is reduced to two dimensional numerical matrix. Terrain structure as well as obstacles are mapped one to oneon to the matrix. Coding is based upon modularity for integrating real sensing and localization inputs into the matrix including error rates. Mathematical formulization is build over Cartesian Coordinate System for computational simplicity. Simulations are made in intense obstacle and severe terrain conditions. The algorithm's performance is tested and the results are discussed. [ABSTRACT FROM AUTHOR]

In this study, the development process, terminology and categories of robot and robotic technology is surveyed and operational requirements of Unmanned Ground Vehicles are presented. Anticipation of military concepts and doctrines about engineering and improvement necessities for the Unmanned Ground Vehicles which has a great importance in future combat systems are evaluated in general and advantages of these very critical systems in todays asymmetric operational conditions are identified. Expected final product parameters from producers and technology companies in unit level are compiled in detail under functional requirements title. [ABSTRACT FROM AUTHOR]

Sensor-centric navigation of unmanned ground vehicles (UGVs) operating in rugged and expansive terrains requires the competency to evaluate the utility of sensor information such that it results in intelligent behavior of the vehicles. Highly imperfect, inconsistent information and incomplete a priori knowledge introduce uncertainty in such unmanned navigation systems. Understanding and quantifying uncertainty yields a measure of useful information that plays a critical role in several robotic navigation tasks such as sensor fusion, mapping, localization, path planning and control. In this article, within a probabilistic framework, the utility of estimation and information-theoretic concepts towards quantifying uncertainty using entropy and mutual information metrics in various contexts of UGV navigation via experimental results is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a disturbance-observer-based control strategy is proposed for longitudinal control and lateral control of unmanned ground vehicles. A composite nonlinear extended state observer is put forward to estimate the total disturbance. A longitudinal dead-zone model is developed with actual constraints and rapidity requirements for unmanned ground vehicles. The disturbance-observer-based feedback controller is presented to improve the robustness of the closed-loop system by incorporating the composite nonlinear extended state observer. Numerical and experimental results are given to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]