Real-time, Closed-Loop and Physics-based Modeling and Simulation System for Unmanned Ground Vehicles in Unstructured Terrain Environments

<title>ABSTRACT</title> <p>Significant advances in sensing, robotics, and wireless networks have enabled the collaborative utilization of autonomous aerial, ground and underwater vehicles for various applications. However, to successfully harness the benefits of these unmanned ground vehicles (UGVs) in homeland security operations, it is critical to efficiently solve UGV path planning problem which lies at the heart of these operations. Furthermore, in the real-world applications of UGVs, these operations encounter uncertainties such as incomplete information about the target sites, travel times, and the availability of vehicles, sensors, and fuel. This research paper focuses on developing algebraic-based-modeling framework to enable the successful deployment of a team of vehicles while addressing uncertainties in the distance traveled and the availability of UGVs for the mission.</p> <p><bold>Citation:</bold> S. Venkatachalam, M. Bansal, J. M. Smereka, “Stochastic Programming Models for Autonomous Ground Vehicles”, In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.</p>

Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.

Modeling and simulation tools have become an integral part of modern engineering processes. In particular, accurate and efficient simulation tools are critical for the design, development, and testing of autonomous unmanned ground vehicles (UGVs). However, because of the complexity of the problem, many UGV simulators are computationally intensive, require expensive hardware to run, and are often not interactive or real-time. Those simulation environments that do provide users with some degree of interactivity and real-time or faster performance gain these features at the sacrifice of simulation fidelity, and these products often provide inadequate results. A new simulation environment for UGV design and development, called the Autonomous Navigation Virtual Environment Laboratory (ANVEL), has been created to address the need for a real-time, interactive, physics-based simulation environment for UGVs. ANVEL is able to meet this need by fusing readily available, off-the-shelf video game technology with high-fidelity, physics-based models. This paper presents the methodology used in developing ANVEL, an example use of ANVEL for development and testing of an autonomous UGV, and plans for the future development.

With the advancement of artificial intelligence technology, unmanned vehicle (UV) systems, including unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV), have been appearing in many scenarios with wide-ranging applications. It is a crucial factor for enhancing human-computer collaboration efficiency to analyze the impact of unmanned vehicle system combinations on operators. The research in this paper presents the effects of different combinations of UAVs and UGVs (1UAV + 1UGV, 1UAV + 2UGVs, 2UAVs + 1UGV, 2UAVs + 2UGVs) on searching performance and operator’s mental workload for accomplishing search tasks. Completion times for tasks and subjective data with operators (N=16) were collected by using Psychopy and questionnaires, respectively. The results in this research indicate that binary growth of controllable unmanned vehicles doesn’t improve the UV utilization rate, but costing longer completion time and increasing operator’s mental workload. Since an excessive number of unmanned vehicles could have a negative impact on task performance, the insights in the paper are helpful for the design of unmanned vehicle systems and the research of human-computer collaboration.

<title>ABSTRACT</title> <p>This paper provides a comparison of the Gazebo and ANVEL simulators and analyzes the aspects of vehicle modeling fidelity that are critical to the design of unmanned ground vehicle (UGV) control and estimation algorithms. The robotic simulators Gazebo [<xref rid="R1" ref-type="bibr">1</xref>], from the Open Source Robotics Foundation (OSRF), and Autonomous Navigation Virtual Environment Laboratory (ANVEL) [<xref rid="R2" ref-type="bibr">2</xref>], from Quantum Signal, are two popular new tools that are being used extensively in academic, commercial, and military development of perception, navigation, and control algorithms for UGVs. Despite the similarities between Gazebo and ANVEL there has been no direct comparison between the two simulators with respect to their validity as vehicle dynamics simulators.</p> <p><bold>Citation:</bold> R. Brothers, D. Bevly, “A Comparison of Vehicle Handling Fidelity Between the Gazebo and ANVEL Simulators”, In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.</p>

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multirobotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this article, given a team of manned ground vehicles (MGVs) and UGVs, the objective is to develop a model that can minimize the number of teams and total distance traveled while considering human–robot interaction (HRI) studies. The human costs of managing a team of UGVs by a MGV are based on human–robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi-objective ground vehicle path planning problem that takes human–robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader–follower framework where a set of UGVs follow an MGV and path for each team such that the missions are completed efficiently.

The unmanned ground vehicle is supposed to replace humans for various applications in both civilian and military area, including transport, delivery, shuttle, clean, patrol, scout, and battle. It has been widely expected that the unmanned ground vehicle will greatly change the human life and the land combat form in the near future. The key techniques of the unmanned ground vehicle are environment perception, motion planning, chassis dynamics control, and cloud control. In this paper, the state of the art of the development of the military and civilian unmanned ground vehicle is introduced, and many classic ones are reviewed in detail in different application categories. The overall design and X-by-wire dynamics control technique of the civilian and military unmanned ground vehicle are reviewed. The X-by-wire technique plays an important role in improving the maneuverability, mobility, and handling performance of the unmanned ground vehicle. Therefore, the chassis dynamics control is considered as a core technique of the X-by-wire unmanned ground vehicle. In addition, as a research trend of the unmanned ground vehicle dynamics control, the cloud-based dynamics control approach of future unmanned ground vehicle based on big data and cloud control techniques is described according to the authors’ work.

In the research reported in this paper, we propose to overcome the unavailability of Global Positioning System (GPS) using combined information obtained from a scanning LADAR rangefinder on an Unmanned Ground Vehicle (UGV) and a LADAR mounted on an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed. The approach to estimate and update the position of the UGV involves registering range data from the two LADARs using a combination of a feature-based registration method and a modified version of the well-known Iterative Closest Point (ICP) algorithm. Registration of range data thus guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Additionally, such registration over time (i.e., from sample to sample), enables position information to be maintained even when both vehicles can no longer maintain GPS contact. The approach has been validated by conducting systematic experiments on complex real-world data.

This study introduces a novel multi-sensor unmanned ground vehicle (UGV) system for monitoring ground deformation adjacent to urban excavation sites. In response to limitations in current monitoring practices and public concerns about visible damage, the research developed a UGV platform equipped with three integrated sensor modules: a Global Navigation Satellite System (GNSS)-Simultaneous Localization and Mapping (SLAM) system for localization, a camera-based photogrammetry unit for vertical displacement analysis, and a line laser scanner for measuring block spacing changes. The system was validated on a 12-m testbed simulating sidewalk pavement with controlled deformation scenarios. Results demonstrated the UGV’s ability to accurately detect and quantify settlement-induced deformations with sub-millimeter precision, even under signal interference conditions. The integrated approach enabled real-time processing and zonal analysis, which improved data stability compared to traditional methods. The proposed Excavation Damage Index effectively visualized deformation severity, offering a practical, scalable, and efficient solution to address gaps in adjacent ground monitoring during urban excavation projects.

Evaluating mobility vs. latency in unmanned ground vehicles

<title>ABSTRACT</title> <p>The U.S. Army Combat Capabilities Development Command (DEVCOM) Ground Vehicle Systems Center (GVSC) has been developing next generation crew stations over the last several decades. In this paper, the problem space that impacts design development and decisions is discussed. This is followed by a historical overview of crewstation development activities that have evolved over the last 30 years, as well as key lessons learned that must be considered for successful ground vehicle Soldier-vehicle interactions. Lastly, the direction and critical technological focus areas are identified to exploit advancements and meet future combat vehicle system needs.</p> <p><bold>Citation:</bold> T. Tierney, “A Perspective on GVSC Crewstation Development and Addressing Future Ground Combat Vehicle Needs,” In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.</p>

The use of unmanned ground vehicles (mobile robots) and unmanned aerial vehicles (drones) in the civil infrastructure asset management sector: Applications, robotic platforms, sensors, and algorithms

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

System modeling and simulation are widely used in robotic vehicle engineering to reduce development time, improve the vehicle design, and miniaturize complex systems. This paper focuses on the steering control of a four-wheel steering vehicle. Optimally-tuned PID controllers are designed for autonomous ground vehicles. The required mission path and stability of steering are achieved by reducing the error between the vehicle heading and the target point. Predefined waypoints are set for the trajectory tracking system. An optimal genetic algorithm (GA) is proposed to tune the vehicle controllers. The proposed GA-tuned-PID controller exhibits satisfactory simulation results and promising experimental results. This paper reports the current status of an ongoing research project for a fully autonomous unmanned ground vehicle (UGV).