Small Unmanned Ground Vehicles Research Articles

DEVELOPMENT AND EVALUATION OF UNMANNED GROUND VEHICLE FOR AUTOMATED ENVIRONMENTAL MONITORING OF LOW-HEIGHT TUNNEL FARMS

Agricultural machinery is an essential tool for improving agricultural production per unit area. It helps farmers to grow more crops in less time and with greater efficiency to meet food security for exponentially growing population. Tunnel farming is an agriculture technique in which the crop is grown in a long row, covered with plastic sheets to maintain favorable environment for crop growth while minimizing environmental and biological hazards. Tunnel farms are divided into low height tunnel, high tunnel, and walk-in tunnel. It is important to monitor tunnels for temperature and relative humidity because these two factors promote disease and pest attack. In low height tunnels, it becomes difficult to physically monitor temperature variations, humidity variation and pest or disease attack. Therefore, it is required to develop suitable solution to this problem which can be provided in the form of small, unmanned ground vehicle (UGV) equipped with necessary sensing devices. In this study, a small UGV having camera, temperature and humidity sensors was developed and evaluated at four different sites. These sites included lemon orchard, research farms, main boulevard and field farm. The temperature and humidity reading were recorded at 8:00, 11:00, 14:00 and 16:00 a day on 10, 20, 30, 40, and 50 ft. length from field boundary. In lemon orchard temperature is minimum (240C) and humidity is maximum (69%) at 8:00 AM. In the evening at 2:00 PM temperature was maximum (390C) and humidity was minimum (45%). In research farm temperature was minimum (280C) and humidity was maximum (65%) at 8:00AM. In the evening at 2:00PM temperature was maximum (430C) and humidity was minimum (40%). In Kfueit boulevard temperature was minimum (250C) and humidity was maximum (69%) at 8:00AM. In the evening at 2:00PM temperature was maximum (400C) and humidity was minimum (46%). In Progressive farmer farm temperature was minimum (300C) and humidity was maximum (69%) at 8:00AM. In the evening at 2:00PM temperature was maximum (430C) and humidity was minimum (46%).

Dynamic Simulation and Experiment of Marching Small Unmanned Ground Vehicles with Small Arms

Dynamic Simulation and Experiment of Marching Small Unmanned Ground Vehicles with Small Arms

Guidance, Navigation and Control System for Multi-Robot Network in Monitoring and Inspection Operations

This work focuses on the challenges associated with autonomous robot guidance, navigation, and control in multi-robot systems. This study provides an affordable solution by utilizing a group of small unmanned ground vehicles and quadrotors that collaborate on monitoring and inspection missions. The proposed system utilizes a potential fields path planning algorithm to allow a robot to track a moving target while avoiding obstacles in a dynamic environment. To achieve the required performance and provide robust tracking against wind disturbances, a backstepping controller is used to solve the essential stability problem and ensure that each robot follows the specified path asymptotically. Furthermore, the performance is also compared with a proportional-integral-derivative (PID) controller to ensure the superiority of the control system. The system combines a low-cost inertial measurement unit (IMU), a GNSS receiver, and a barometer for UAVs to generate a navigation solution (position, velocity, and attitude estimations), which is then used in the guidance and control algorithms. A similar solution is used for UGVs by integrating the IMU, a GNSS receiver, and encoders. Non-linear complementary filters integrate the measurements in the navigation system to produce high bandwidth estimates of the state of each robotic platform. Experimental results of several scenarios are discussed to prove the effectiveness of the approach.

Preliminary study on the application of robotic herding to manipulation of grazing distribution: Behavioral response of cattle to herding by an unmanned vehicle and its manipulation performance

Robotic herding is attracting attention as a new technology for managing grazing animals. As a preliminary step toward the application of robotic herding to the manipulation of grazing distribution, we investigated the behavioral responses of cattle to herding by an unmanned vehicle and its performance in manipulating grazing distribution in a pasture. A herd of approximately 30 cows was stocked in a 1.1-ha pasture for 5 days each month from May to October 2021. The cows were herded by a small unmanned ground vehicle for 7 days of the periods. The pasture was divided into nine plots, with one–four plots assigned as the herding area. When the cows grazed in the herding area, the operator maneuvered the vehicle using a transmitter to move them out of the area. The initial reaction (walk away, appear startled, appear interested, back away, or ignore) to the approaching vehicle, flight distance (within one cow-length or more), whether cows escaped from the vehicle or not, and escape gait (walk or run) were classified based on videos recorded during herding. The behavior and location of the cows were observed at 5 min intervals during the time in the pasture. Utilization rates (percentage of grazing time) of the plots were compared with those on days without herding. Throughout the survey, cows initially walked away from the approaching vehicle in 78% of the cases, the flight distance was within one cow-length in 89%, cows escaped from the vehicle in 93%, and when escaping, they walked in 91%. On the first day of herding, these occasions were less frequent (58%, 83%, 84%, and 82%, respectively), but increased on the second day and remained high thereafter. These findings suggest that cows were able to adjust to herding by an unmanned vehicle within a day and respond to the vehicle desirably for the manipulation of grazing distribution with little fear, irritation or discomfort. One-plot herding decreased the use of the herding area from 10.5% to 3.3% (P = 0.001). Two-plot herding decreased the use from 22.3% to 10.4% (P = 0.015) and increased the use of the least-utilized plot from 8.2% to 14.5% (P = 0.021). These findings indicate that robotic herding by an unmanned vehicle has the potential to positively manipulate grazing distribution.

Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Ionizing Radiation Sensor for Nanosatellites, Microdrones and Small Unmanned Ground Vehicles

The purpose of this paper is to present an ionizing radiation sensor suitable for nanosatellites, small drones and other types of unmanned vehicles. When implemented on an airborne or ground-based unmanned vehicle the sensor is beneficial in disaster management scenarios such as inspection of buildings and facilities for ionizing radiation contamination and measurements of the ionizing radiation dose. Herein, a design and laboratory tests of the sensor are presented. The instrument is a lightweight module having low power consumption suitable for nanosatellite platforms and for mobile use by having it installed in an unmanned system (airborne or ground-based). For test purposes a 36-rotor drone was developed along with a mobile ground based unmanned vehicle. With all implementations the radiological sensor is directly connected to the main processor unit of the platform. In the case of the drone and ground-based vehicle systems the processor unit is the microcontroller of the autopilot. Experimental results of laboratory measurements of different radiation sources are shown and discussed. The experimental setup demonstrates a few advances related to specific problems encountered in the existing ionizing radiation measurement systems.

Experimental research on the muzzle response characteristics of small unmanned ground vehicles with small arms

The muzzle response is the main feature affecting the firing accuracy of weapons. To research the muzzle response characteristics of small unmanned ground vehicles with small arms (SUGVsSA) during shooting, this paper designs a test method that combines an inertial measurement system (IMS) with a high-speed photogrammetric system (HSPS) to measure the muzzle response. That is, an inertial measurement unit (IMU) is fixed onto the gun body to record the three-dimensional angular motion of the barrel; meanwhile, a high-speed camera is used to capture the characteristic markers of the unmanned ground vehicle from the side. After data processing, the muzzle response curves during four consecutive firings when the vehicle is running at different speeds and firing angles are obtained. Considering the presence of noise in muzzle response signals, the wavelet threshold de-noising (WTD) algorithm based on a novel variable threshold function is used to de-noise the test signal. The processing results demonstrate that the WTD algorithm based on the novel variable threshold function can not only suppress noise in the muzzle response signal but also retain the local details of the signal. The combination of the IMS and HSPS complements the muzzle response data and can comprehensively and accurately reflect the muzzle response characteristics of SUGVsSA. As the vehicle speed and firing angle increase, the muzzle vibration intensifies, only when the vehicle speed is 0.3 m/s, and the muzzle maximum elevation angle displacement after each firing decreases when it is stationary. The results presented in this paper may provide a workable reference for understanding the muzzle response characteristics of SUGVsSA and evaluating the firearm compatibility of other unmanned systems.

Using perception cues for context-aware navigation in dynamic outdoor environments

Continued advancements in robot autonomy have allowed the research community to shift from using robots as tools in the field to deploying robot teammates capable of learning, reasoning, and executing tasks. Autonomous navigation is one necessary capability of a robot teammate that must operate in large field environments. In relatively static environments a simple navigation solution such as obstacle avoidance along the shortest path may suffice; however, as robot teammates are deployed to highly dynamic environments with changing mission requirements, additional environment context may be necessary to ensure safe and reliable navigation. Although recent works in urban autonomous driving have advanced the state-of-the-art in context-aware decision making, the spectrum of behaviors deployed for context-switching is more narrowly focused (by defining constraints specific to operation in structured environments) than what might be required for human-agent teaming field missions. As such, establishing a context-aware intelligent system for dynamic, unstructured environments is still an open problem. We discuss our approach to the integration of several context-aware navigation behaviors on a small unmanned ground vehicle (UGV) and a perception stack that provides cues used to transition between these different learned behaviors. Specifically, we integrate socially compliant, terrain-aware, and covert behaviors in an outdoor navigation scenario where the UGV encounters moving pedestrians, different terrains, and weapon threats. We provide a detailed account of the overall system integration, experiment design, component- and system-level analysis, and lessons learned.

CHAP: Cotton-Harvesting Autonomous Platform

The US cotton industry provided over 190,000 jobs and more than $28 billion total economic contributions to the United States in 2012. The US is the third-largest cotton-producing country in the world, following India and China. US cotton producers have been able to stay competitive with countries like India and China by adopting the latest technologies. Despite the success of technology adoption, there are still many challenges, e.g., increased pest resistance, mainly glyphosate resistant weeds, and early indications of bollworm resistance to Bt cotton (genetically modified cotton that contains genes for an insecticide). Commercial small unmanned ground vehicle (UGV) or mobile ground robots with navigation-sensing modality provide a platform to increase farm management efficiency. The platform can be retrofitted with different implements that perform a specific task, e.g., spraying, scouting (having multiple sensors), phenotyping, harvesting, etc. This paper presents a proof-of-concept cotton harvesting robot. The robot was retrofitted with a vacuum-type system with a small storage bin. A single harvesting nozzle was used and positioned based on where most cotton bolls were expected. The idea is to create a simplified system where cotton bolls′ localization was undertaken as a posteriori information, rather than a real-time cotton boll detection. Performance evaluation for the cotton harvesting was performed in terms of how effective the harvester suctions the cotton bolls and the effective distance of the suction to the cotton bolls. Preliminary results on field test showed an average of 57.4% success rate in harvesting locks about 12 mm from the harvester nozzle. The results showed that 40.7% was harvested on Row A while 74.1% in Row B for the two-row test. Although both results were promising, further improvements are needed in the design of the harvesting module to make it suitable for farm applications.

Development Trends of Small Unmanned Ground Vehicles in Technology Leading Countries

Development Trends of Small Unmanned Ground Vehicles in Technology Leading Countries

Simulation Method for the Transport System of a Small-Sized Reconfigurable Mobile Robot

This study focuses on the features of transport (locomotion) systems of mobile mini-robots (MMR), i.e., small unmanned ground vehicles of a portable type measuring several tens of centimeters and weighing no more than 15 kg. A distinctive feature of the considered MMR is the possibility of its both structural reconfiguration (i.e., the ability to function in two options—tracked and wheeled) and geometric reconfiguration in the tracked option (i.e., chassis geometry variation). Thus, the transport system of such a mobile robot is divided into two components: a locomotion subsystem and a chassis geometry variation subsystem. The article examines the factors that are necessary for the correct mathematical description of such a small-sized and relatively high-speed transport system. A method for constructing a computer model of the transport system as an electromechanical device only is proposed. Such a computer model of the MMR transport system is developed for two types of chassis configurations: tracked and wheeled. The experimental studies performed and the comparison of the experimental and simulated data obtained showed their close convergence, within 5 to 7%. Thus, it is shown that the revealed features of the MMR transport systems along with the proposed method for their computer model development considering these features make it possible to increase the accuracy and adequacy of the MMR motion simulation in comparison with previously known approaches used in the computation of larger vehicles. The results obtained make it possible to consider the proposed computer model of the transport system as an electromechanical component of the complete model of a mobile robot.

Vector field path-following control for a small unmanned ground vehicle with Kalman filter estimation

Industry 4.0 accelerates the growth of unmanned technology that reduces the labor cost and creates high automation in manufacturing system. The automated guided vehicle which is capable of transferring materials or executing tasks without human intervention becomes a necessary system for modern unmanned factories. The study explores the guidance and control design to accomplish the common task of path-following control for unmanned ground vehicles (UGV). A complete design method is presented that includes the lateral-directional autopilot, the vector field guidance for path-following, and multi-sensor fusion. The lateral-directional autopilot produces the low-level control action, the higher level guidance indicates the course direction of UGV at every spatial point based on the lateral path error, and the accurate UGV position relies on the estimate obtained by dynamically fusing sensors with extended Kalman filter. The design parameters in every stage are analyzed theoretically first and then fine-tuned in practice. The process is clearly described in this study, and the field test results are discussed in details to verify the performance of the proposed method and demonstrate the superiority over others.

Real‐time keypoints detection for autonomous recovery of the unmanned ground vehicle

The combination of a small unmanned ground vehicle (UGV) and a large unmanned carrier vehicle allows more flexibility in real applications such as rescue in dangerous scenarios. The autonomous recovery system, which is used to guide the small UGV back to the carrier vehicle, is an essential component to achieve a seamless combination of the two vehicles. This paper proposes a novel autonomous recovery framework with a low-cost monocular vision system to provide accurate positioning and attitude estimation of the UGV during navigation. First, we introduce a light-weight convolutional neural network called UGV-KPNet to detect the keypoints of the small UGV from the images captured by a monocular camera. UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time. Then, six degrees of freedom pose is estimated using the detected keypoints to obtain positioning and attitude information of the UGV. Besides, we are the first to create a large-scale real-world keypoints dataset of the UGV. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of both accuracy and speed on UGV keypoint detection, and can further boost the 6-DoF pose estimation for the UGV.

Electrically Small Platform-Based Antennas for an Unmanned Ground Vehicle

We present a design strategy for platform-based antennas operating on electrically small platforms such as unmanned ground vehicles (UGVs). Specifically, we report the design of a platform-based antenna operating at the lower end of the very-high-frequency (VHF) band for a small UGV with physical dimensions of 99 cm × 67 cm × 39 cm. The antenna is mounted on the bottom of the platform resulting in an extremely low-profile design and generating vertically polarized, monopole-like radiation patterns. Using characteristic-mode theory (CMT), performance metrics of the antenna including its maximum potential bandwidth and radiation patterns are investigated. The performance of the antenna when operating in a realistic operational environment is analyzed using full-wave electromagnetic simulations. A full-scale prototype of the antenna mounted on the bottom of the vehicle was fabricated and experimentally characterized. Finally, the design of this low-profile antenna is extended to a two-element antenna array mounted on the same platform and a prototype of this two-element array was fabricated. Basic experiments were conducted to study impedance matching and mutual coupling effects in this electrically small antenna array. The performance of this array as part of a two-element direction-finding system was investigated through computer simulations.

소형 지상로봇 플랫폼 개발

소형 지상로봇 플랫폼 개발

Decentralized Algorithms for Weapon‐Target Assignment in Swarming Combat System

Swarming small unmanned aerial or ground vehicles (UAVs or UGVs) have attracted the attention of worldwide military powers as weapons, and the weapon‐target assignment (WTA) problem is extremely significant for swarming combat. The problem involves assigning weapons to targets in a decentralized manner such that the total damage effect of targets is maximized while considering the nonlinear cumulative damage effect. Two improved optimization algorithms are presented in the study. One is the redesigned auction‐based algorithm in which the bidding rules are properly modified such that the auction‐based algorithm is applied for the first time to solve a nonlinear WTA problem. The other one is the improved task swap algorithm that eliminates the restriction in which the weights of the edges on graph G must be positive. Computational results for up to 120 weapons and 110 targets indicate that the redesigned auction‐based algorithm yields an average improvement of 37% over the conventional auction‐based algorithm in terms of solution quality while the additional running time is negligible. The improved task swap algorithm and the other two popular task swap algorithms almost achieve the same optimal value, while the average time‐savings of the proposed algorithm correspond to 53% and 74% when compared to the other two popular task swap algorithms. Furthermore, the hybrid algorithm that combines the above two improved algorithms is examined. Simulations indicate that the hybrid algorithm exhibits superiority in terms of solution quality and time consumption over separately implementing the aforementioned two improved algorithms.

Laser-Pointer Guidance System for Small Unmanned Ground Vehicle in Disaster Area

We are developing a small unmanned ground vehicle to support rescue missions by exploring sediment-related disaster areas. To simplify the vehicle operation, we employ a laser pointer as guidance system. In this paper, we describe a method to detect the laser-pointer spot under sunlight and a fuzzy learning method for guiding the vehicle towards the corresponding target position. To verify the spot detection, we performed experiments in laboratory emulating sunlight conditions, whereas to validate the guidance method, we developed a fuzzy control system whose input-variable ranges were determined from preliminary experiments and whose outputs include turning, backward, and forward motions of the vehicle. Likewise, experimental results confirm the effectiveness of the proposed guidance method.

Machine vision for orchard navigation

Developing a machine vision based autonomous utility vehicle for agricultural application is a challenging task due to changing physical landmarks. While most research thus far has developed algorithms that take advantage of ground structures such as trunks and canopies in the orchard, this research uses the combination of the canopy with the background sky. By focusing on the tree canopy and sky of an orchard row, an unmanned ground vehicle can extract features that can be used for autonomously navigating through the center of the tree rows. This was attempted by using a small-unmanned ground vehicle platform driven by four motors and guided by a machine vision system. The machine vision system is composed of a multispectral camera to capture real-time images and a personal computer to process the images and obtain the features used for autonomous navigation. Laboratory field tests showed that the small vehicle platform system was able to navigate autonomously with an RMS error of 2.35 cm. Field tests using a peach orchard showed that the small vehicle platform system could navigate the rows autonomously with an RMS error of 2.13 cm. The machine vision algorithm developed in this study has the potential to guide small utility vehicles in the orchard in the future.

The role of robots in the battlefields of the future

Purpose This paper aims to provide an overview of robots presently in use by the military and an insight into some that are under development. Design/methodology/approach Following a short introduction, this paper first considers existing applications of robots in the military field, including details of Russian weaponised ground robots. It then highlights a range of military robot developments and concludes with a brief discussion. Findings Drones (unmanned aerial vehicles) and small unmanned ground vehicles (UGVs) are among the most widely used robots by the military. Russia is developing a growing armoury of heavily weaponised UGVs, some of which were recently deployed in Syria. Some topics of development include humanoid robots, powered exoskeletons, load-carrying robots, micro-air vehicles and autonomous land vehicles. Robots will play an ever-growing role in military actions, and while some developments offer longer-term prospects, others are expected to be deployed in the near future. Originality/value Robots are playing an increasingly important role in military conflicts, and this provides details of present-day and anticipated future uses of robots by the military.

Joint localization and target tracking with a monocular camera

Localization capabilities are necessary for autonomous robots that need to keep track of their position with respect to a surrounding environment. A pursuit robot is an autonomous robot that tracks and pursues a moving target, requiring accurate localization relative to the target’s position and obstacles in the local environment. Small unmanned ground vehicles (SUGVs) equipped with a monocular camera and wheel encoders could act as effective pursuit robots, but the noisy 2D target position and size estimates from the monocular camera will in turn lead to overly noisy 3D target pose estimates. One possible approach to relative localization for pursuit robots is, rather than simply tracking and estimating a relative robot–target position in each frame, joint localization, in which the purser and target are both localized with respect to a common reference frame. In this paper, we propose a novel method for joint localization of a pursuit robot and arbitrary target. The proposed method fuses the pursuit robot’s kinematics and the target’s dynamics in a joint state space model. We show that predicting and correcting pursuer and target trajectories simultaneously produces improved results compared to standard filters for estimating relative target trajectories in a 3D coordinate system. For visual tracking, we also introduce an adaptive histogram matching threshold for suspending tracking when the target is lost in a cluttered environment. When tracking is suspended, rather than traversing the entire image to search for a reappearance of the target, we only search the part of the image segmented by histogram backprojection and correctly reinitialize the tracker. The experimental results show that the joint localization method outperforms standard localization methods and that the visual tracker for pursuit robot can deal effectively with target occlusions.