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In recent years, militaries have strengthened efforts to integrate unmanned technologies to improve manned-unmanned teaming (MUM-T) capabilities. As some countries’ fighting-age populations are decreasing, militaries are turning to readily available, cost efficient, and sophisticated unmanned technologies. MUM-T holds great potential not only to alleviate manpower shortages in militaries, but also to improve combat capabilities. This thesis studies the effectiveness of MUM-T at the frontline, down to infantry teams supporting offensive operations in urban terrain. An agent-based simulation is used to model a MUM-T combat operation with and without an unmanned ground vehicle (UGV) to support an infantry company. An analysis was conducted on more than 76,800 simulated battles. It was observed that MUM-T concepts could dramatically increase combat effectiveness, as assessed by increased enemy casualties. The UGV reloading time, weapon accuracy, and own force structure were also observed to significantly impact the infantry’s lethality and survivability. This analysis concludes that implementation of MUM-T at the small-unit tactical level has great potential to enhance overall combat performance. Moving forward, combat models could be integrated into future military exercises such that the findings from simulations can be verified and validated. Major, Singapore Army Approved for public release. Distribution is unlimited.

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.

Autonomous mobile robots (AMR) like ground and aerial vehicles may encounter internal failures and external disturbances when deployed in real-world scenarios compromising the success of a mission. This thesis proposes an online learning method to adapt the motion planner to recover and continue an operation after a change in a robot's dynamics. Our proposed framework builds on the Markov Decision Process (MDP) and leverages the residual - defined in this work as the difference between the predicted and the actual state - to update the transition probabilities online and in turn update the optimal MDP policy. To maintain the system safe during learning, we propose a chi-squared-based dynamic learning rate that is event-triggered when the robot approaches an unsafe region of the workspace. Our framework can also distinguish between external disturbances versus internal failures by tracking the robot's state in a local and fixed frame view. We finally propose a state-machine-based resetting procedure to return to a previous MDP model when the problem disappears. This framework for resilient planning of impaired vehicles is validated both in simulations and experiments on unmanned ground vehicles (UGV) in a cluttered environment. Finally, we show an extension of our framework for multitask cooperative missions in which robots need to balance tasks based on the impaired dynamics of the robots in the network.

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.

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.

The system of interest (SOI) for this research centers on the mission of a friendly Blue Force (BF) controlling the enemy Red Force's (RF) influence on a strategic, bottleneck trade route and the execution of the kill chain functions performed by the BF during the conflict that ensues. The kill chain function of 'finding the RF threat' is conducted by the BF's Intelligence Surveillance Reconnaissance (ISR), the function of 'targeting the RF threat' is performed by the BF's decision maker, and the function of 'engaging the RF threat' is conducted by the BF's shooter. There are multiple messages exchanged between the BF's ISR, decision maker, and shooter during execution of the kill chain functions and a recognized need to improve the command and control (C2) responsiveness while performing cooperative engagements to ensure overmatch of a near peer enemy by 2030, when naval unmanned surface vessels are predicted to join the fleet. Major, United States Marine Corps Civilian, Department of the Army Civilian, Department of the Army Civilian, Department of the Army Approved for public release. distribution is unlimited

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

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

In order to continue the Unmanned Tactical Autonomous Control and Collaboration (UTACC) program, an accurate understanding of the current unmanned vehicles (UxVs) available on the commercial market is required. This thesis used site visits, conference attendance, trade shows, and online databases to evaluate and rank both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This thesis identified 10 out of 46 UAVs and five UGVs that could be quick-wins for the Marine Corps if adopted. These identified platforms received the highest scores based on five measures of effectiveness (MOEs): technology readiness level (TRL), mission capability, interdependence, modularity, and security. This thesis also addressed the need to evaluate systems based on their level of interdependence instead of level of autonomy, acknowledging the importance of human-machine teaming. CRUSER https://hdl.handle.net/10945/60380 Captain, United States Marine Corps Captain, United States Marine Corps Approved for public release; distribution is unlimited.

Autonomous robotic systems often encounter varying degrees of uncertainty. Characteristics of an environment or mission, such as the definition of a terrain, the presence of an attacker, or the necessary sub-goals may have dramatic impacts on mission performance. Guaranteeing reliable execution demands an analysis of these uncertainties in order to ensure some form of robust operation, such as continual performance in the presence of failures. In this thesis, we present contributions related to robust operations of multi-robot coordination and autonomous exploration and an in-depth analysis of LiDAR-related uncertainties.

This paper describes the design and implementation of a controller to track the desired heading angle for an unmanned ground vehicle (UGV) considering the limits on rotation of steering wheel and steering motor rate. The well-known �bicycle model� approximation, that considers the vehicle slip angle and ground-wheel interaction, has been used. In this work, combined closed loop control for steering motor and heading angle of the vehicle has been considered for better accuracy. The challenge in designing this control system is to include the actuator dynamics as the response time to steer the front wheel is in the same order as that of the heading angle dynamics of the vehicle. Experiments have been conducted, analysis of data collected is presented and the resulting mathematical representation is explained in detail. Results from both simulation and experimental implementation are also compared. It has been found that the vehicle controller can be tuned effectively to achieve the desired heading angle changes up to twenty degrees within three seconds when the vehicle is moving at a speed of 1.4 m/sec. � 2015, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

In this paper we show the developing and the applications of a remotely controlled terrestrial vehicle (or Unmanned Ground Vehicles--UGV) provided with an integrated sensory system for environmental monitoring. The developed system is aimed to monitor some of the key air pollutants and harmful compounds, usable in contaminated sites at high risk to human health. The system is a 4WD radio-controlled vehicle, with small dimensions and low weight, complemented by a sensory system based on hybrid sensors technology (e.g. optic, electrochemical, gravimetric, etc...). These features allow this system to be easily used when inspection missions are required, before or after any environmental disaster. It is able to measure, in few minutes, the atmospheric particulate matter (PM1, PM2.5, PM10), CO2 and CO, H2S, SO2, NO2 in the range between 15 and 5000 ppb. Moreover, it is equipped with a telemetry system for the remote-controlled navigation, including a high-resolution camera, a GPS antenna, an anemometer and proximity sensors. The core of the device is a microprocessor board able to assist the navigation, acquiring values from sensors, transfer/record data and control tens input/outputs up to a distance of 1 km. In this work we report some information regarding the integration, the calibration and the data related to the monitoring of a waste landfill in the closing phase, during the construction of the extractor for generated biogas.

Despite the growing popularity of unmanned systems being deployed in the military domain, limited research efforts have been dedicated to the progress of ground system developments. Dedicated efforts for unmanned ground vehicles (UGV) focused largely on operations in continental environments, places where vegetation is relatively sparse compared to a tropical jungle or plantation estate commonly found in Asia. This research explore methods for the development of an UGV that would be capable of operating autonomously in a densely cluttered environment such as that found in Asia. This thesis adopted a systems engineering approach to understand the pertinent parameters affecting the performance of the UGV in order to evaluate, design and develop the necessary guidance, navigation and control algorithms for the UGV. The thesis uses methodologies such as the pure pursuit method for path following and the vector field histogram method for obstacle avoidance as the main guidance and control algorithm governing the movement of the UGV. The thesis then considers the use of feature recognition method of image processing to form the basis of the target identification and tracking algorithm. http://archive.org/details/systemsengineeri1094550579 Outstanding Thesis Major, Republic of Singapore Army Approved for public release; distribution is unlimited.

Актуальность. Ведущие технологически развитые страны (в том числе США, Россия, Великобритания, Франция, Китай, Израиль, Южная Корея), активно разрабатывают робототехнические комплексы, способные с высокой степенью автономности вести боевые действия без участия человека. Так, в Вооруженных силах США планируется, что к 2030 г. доля беспилотных и безэкипажных средств составит 30% от общего состава боевых машин. По оценкам американских военных специалистов, боевые возможности подразделений оснащенными робототехническими комплексами возрастут в 2-2,5 раза. При этом можно констатировать, что их широкое применение приведет к коренному пересмотру основных принципов ведения войны, с учетом не только военно-технических ее аспектов, но и с учетом ее социально-психологических факторов. В связи с этим, актуальным является анализ современного состояния и перспектив развития робототехнических комплексов военного назначения. Целью работы является анализ современного состояния и перспектив развития робототехнических комплексов военного назначения, а также выявление общих и частных закономерностей их развития. Особое внимание уделено робототехническим комплексам на основе: беспилотных летательных аппаратов, наземных дистанционно-управляемых машин, надводных и подводных необитаемых аппаратов. Новизна. Элементом новизны работы является выявленные общие тенденции развития робототехнических комплексов военного назначения. Также к элементам новизны, стоит отнести выявленные частные тенденции развития этих комплексов с учетом специфики их применения для решения боевых задач в воздушной, наземной и морской (надводной и подводной) сферах. Практическая значимость. Представленный анализ может быть использован техническими специалистами для обоснования новых технологических решений в области робототехники, а также военными специалистами для обоснования новых форм и способов вооруженной борьбы с учетом перспектив развития робототехнических комплексов военного назначения. Кроме того, данный анализ будет полезна научным работникам и соискателям, ведущим научные исследования для прикладного обоснования целесообразности предлагаемых ими улучшений военно-технических систем, за счет использования робототехнических средств., Relevance. Leading developed countries are developing robots which are able to combat operations without human intervention. The U.S. armed forces expect that the proportion of robots will be 30% of the total composition of combat vehicles by 2030. Thus, the combat capabilities of the units equipped with robots will increase in 2-2,5 times. Wide application of military robots will change the basic principles of warfare. These changes concern the technical aspects, the human-robot interaction, military tactics and strategy. Therefore, the analysis of the current state and prospects of improvement of robots for military purposes is relevant. The aim of this paper is the analysis of the current state and prospects of improvement of robots for military purposes. Novelty. The element of novelty of this paper is the general trend of improvement of robots for military use which have been identified in the analysis. Also novelty items, is trends of improvement of these robots in case their use in air, ground and sea (surface and underwater) areas. Practical relevance. The analysis presented can be used by technical specialists to justify new technological solutions in the field of robotics. Also, this analysis can be used military specialists for elaborate new forms and methods of armed struggle with the use of military robots.

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

Unmanned ground vehicles (UGVs) are increasingly being used for civilian and military applications. Passive sensing, such as visible cameras, are being used for navigation and object detection. An additional object of interest in many environments is text. Text information can supplement the autonomy of unmanned ground vehicles. Text most often appears in the environment in the form of road signs and storefront signs. Road hazard information, unmapped route detours and traffic information are available to human drivers through road signs. Premade road maps lack these traffic details, but with text localization the vehicle could fill the information gaps. Leading text localization algorithms achieve ~60% accuracy; however, practical applications are cited to require at least 80% accuracy [49]. The goal of this thesis is to test existing text localization algorithms against challenging scenes, identify the best candidate and optimize it for scenes a UGV would encounter. Promising text localization methods were tested against a custom dataset created to best represent scenes a UGV would encounter. The dataset includes road signs and storefront signs against complex background. The methods tested were adaptive thresholding, the stroke filter and the stroke width transform. A temporal tracking proof of concept was also tested. It tracked text through a series of frames in order to reduce false positives. Best results were obtained using the stroke width transform with temporal tracking which achieved an accuracy of 79%. That level of performance approaches requirements for use in practical applications. Without temporal tracking the stroke width transform yielded an accuracy of 46%. The runtime was 8.9 seconds per image, which is 44.5 times slower than necessary for real-time object tracking. Converting the MATLAB code to C++ and running the text localization on a GPU could provide the necessary speedup. Master of Science

The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation across the entire image. This algorithm, named IRIS, was developed and tested on several sets of roadway images. The algorithm was tested for its accuracy and speed, and was found to be better than 86% accurate across all data sets for an optimal choice of algorithm parameters. IRIS was also found to be faster than a k-means segmentation across the entire image. IRIS was found to be adequate for fulfilling the design goals for the algorithm. IRIS is a feasible system for lane identification and segmentation, but it is not currently a viable system. More work to increase the speed of the algorithm and the accuracy of lane detection and to extend the inherent lane model to more complex road types is needed. IRIS represents a significant step forward in the single camera roadway perception field. Master of Science

Current trends in agricultural equipment have led to an increasing degree of autonomy. As the state of the art progresses towards fully autonomous vehicles, it is important to consider assumptions implicit in the design of these vehicles. Current automation in harvesters have led to increased sensing and automation on current combines, but no published research examines the effect of machine size on the viability of the autonomous system. The question this thesis examines is: if a human is no longer required to operate an individual harvester, is it possible to build smaller equipment that is still economically viable? This thesis examines the appropriateness of automating these machines by developing a conceptual model for smaller, fully autonomous harvesters. This model includes the basic mechanical subsystems, a conceptual software design, and an economic model of the total cost of ownership. The result of this conceptual design and analysis is a greater understanding of the role of autonomy in harvest. By comparing machine size, machine function, and the costs to own and operate this equipment, design guidelines for future autonomous systems are better understood. It is possible to build an autonomous harvesting system that can compete with current technologies in both harvest speed and overall cost of ownership. Master of Science

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

Unmanned ground vehicles are increasingly employing a combination of active sensors such as LIDAR with passive sensors like cameras to perform at all levels of perception, which includes detection, recognition and classification. Typical cameras measure the intensity of light at a variety of different wavelengths to classify objects in different areas of an image. A polarimetric camera not only measures intensity of light, but can also determine its state of polarization. The polarization of light is the angle the electric field of the wave of light takes as it travels. A polarimetric camera can identify the state of polarization of the light, which can be used to segment highly polarizing areas in a natural environment, such the surface of water. The polarimetric camera designed and built for this thesis was created with low cost in mind, as commercial polarimetric cameras are very expensive. It uses multiple beam splitters to split incoming light into four machine vision cameras. In front of each machine vision camera is a linear polarizing filter that is set to a specific orientation. Using the data from each camera, the Stokes vector can be calculated on a pixel by pixel basis to determine what areas of the image are more polarized. Test images of various scenes that included running water, standing water, mud, and vehicles showed promise in using polarization data to highlight and identify areas of interest. This data could be used by a UGV to make more informed decisions in an autonomous navigation mode. Master of Science

Perception is a key feature in how any creature or autonomous system relates to its environment. While there are many types of perception, this thesis focuses on the improvement of the visual robotics perception systems. By implementing a broadband passive sensing system in conjunction with current perception algorithms, this thesis explores scene reconstruction and world modeling. The process involves two main steps. The first is stereo correspondence using block matching algorithms with filtering to improve the quality of this matching process. The disparity maps are then transformed into 3D point clouds. These point clouds are filtered again before the registration process is done. The registration uses a SAC-IA matching technique to align the point clouds with minimum error. The registered final cloud is then filtered again to smooth and down sample the large amount of data. This process was implemented through software architecture that utilizes Qt, OpenCV, and Point Cloud Library. It was tested using a variety of experiments on each of the components of the process. It shows promise for being able to replace or augment existing UGV perception systems in the future. Master of Science