Unmanned Ground Vehicle System Research Articles

Switching formation strategy with the directed dynamic topology for collision avoidance of a multi‐robot system in uncertain environments

This paper tackles the distributed leader–follower cooperative control problem for networked heterogeneous unmanned aerial vehicle–unmanned ground vehicle (UAV-UGV) systems in unknown environments requiring formation keeping, obstacle avoidance, inter-robot collision avoidance, and reliable robot communications. To adopt various formations, we design a novel negative imaginary (NI) switching formation protocol with a directed dynamic topology. To prevent an inter-mobile robot collision, a new method to formulate the virtual propulsive force between robots is employed. To avoid unexpected obstacles, a new obstacle avoidance technique that allows the UGVs' formation to change its shape and the UGVs' roles is developed. To determine each UGV robot's order in obstacle avoidance formation, a quadrotor UAV, controlled by a strictly negative imaginary controller involving good wind resistance characteristics, tracks the center of formation shape to guarantee the maintaining visibility for multi-robot systems on the ground. The proposed control system's efficacy is investigated through a rigorously comparative study with other control techniques, namely, the performance of artificial potential field and an NI obstacle avoidance strategy using the switching formation control method without switching topology. Finally, we also conduct a stability analysis of the closed-loop control system using the NI-systems theory.

Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems.

Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.

Synchronization of Network Systems via Aperiodic Sampled-Data Control With Constant Delay and Application to Unmanned Ground Vehicles

In this paper, the synchronization issue for network systems with nonlinear dynamics is considered. Together with zero-order holder, the aperiodic sampled-data control law is utilized. Compared with the traditional periodic sampled-data control method, this approach demonstrates more greater flexibility. Adopting input delay approach, the initial sampled-data system is remodeled by continuous time system involving time-varying delays in the control signals. For the purpose of designing the sampling controllers suffering constant delays, an updated Lyapunov functional is developed from the augmentation of Wirtinger's inequality. Such a Lyapunov functional results in efficient and simplified synchronization conditions. A sufficient condition for synchronizability of network systems is set up. Then, for the case of unstable systems with some constant delays, a fresh discretized Lyapunov functional is introduced. Finally, we utilize the numerical simulation outcomes to prove the efficacy and advantage of our algorithm. Moreover, based on the network unmanned ground vehicle systems, the experiment results in a real scenario are provided to illustrate the effectiveness of the designed synchronization scheme.

An ICI Suppression Analysis Testbed for Harbor Unmanned Ground Vehicle Deployment

In recent years, intelligent transport system (ITS) applications with unmanned ground vehicles (UGV) have increased dramatically to improve the safety, efficiency, and unique services in low- to medium-range communication deployment. Orthogonal frequency division multiplexing (OFDM) is adopted as a modulation scheme for this communication system to combat fading. Unfortunately, because of possible high UGV speed, the orthogonality between subcarriers in OFDM signals is prone to intercarrier interference (ICI) caused by the Doppler spread. This paper discusses ICI suppression analysis using Universal Software Radio Peripheral (USRP) N210 under several Harbor transmission scenarios. Such communication links may contain line of sight (LoS) components in transmission. The channel is thus modeled as the well-known fast time varying path loss model. Furthermore, this paper derives the signal to interference ratio (SIR) and evaluates this parameter for several types of fading and Doppler spread shapes in UGV channels. Additionally, the paper presents a practical method for calculating the spectral efficiency of OFDM systems, with an example for actual implementation in UGV systems. Eventually, the testbed results indicate that the SIR will decrease if the maximum Doppler spread expands. In the Rician channel, it can be seen that the SIR depends on the maximum Doppler speed, the angle of arrival (AoA), and even the Rician element of the LoS component.

A Survey on Fractional Order Control Techniques for Unmanned Aerial and Ground Vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade.

Operator Suspicion and Human-Machine Team Performance Under Mission Scenarios of Unmanned Ground Vehicle Operation

Emergent cyber-attack threats against cyber-physical systems can create potentially catastrophic impacts. The operators must intervene at the right moment when suspected attacks occur, without over-reliance on systems to detect the cyber-attacks. However, military operators are normally trained to trust, rather than suspect systems. We applied suspicion theory to explore how operators detect and respond to cyber-attacks against an unmanned ground vehicle (UGV) system in the operational context of a human–machine team (HMT). We investigated the relationships between the operator suspicion and HMT performance by conducting human-in-the-loop experiments on eight mission scenarios with 32 air-force officers. The experiment yielded a significant, negative relationship between operator suspicion and HMT performance (quantified both in terms of the desirability of decision response and the time to respond). Notably, operator suspicion increased with the combined effects of cyber-attacks and a sentinel alert but not with the alert alone. This finding was particularly meaningful for “false-negative” scenarios, in which no sentinel alert was sent despite cyber-attacks having occurred. Although the operators did not receive an alert, the operators grew more suspicious, seeking more information; it took longer for the operators to respond, and their decision responses were highly divergent (17.2% came with less-desirable responses, and 21.9% were considered instances of over-reliance). In contrast, in “false-positive” scenarios, 95.3% of the operator responses were highly desirable. This experiment has implications for the role of a sentinel alert in engineering trustworthy HMT systems so that the operators can quickly transition through state-suspicion to the most desirable decision.

Unmanned Ground Vehicle with Stereoscopic Vision for a Safe Autonomous Exploration

At present there are several systems in cars that provide assistance to the driver and the tendency is that these systems are increasingly efficient and that for their operation do not require the intervention of the driver. Computer vision is relevant in this sector due to the contribution it provides, for example, through the treatment of images algorithms are designed for the detection of objects, pedestrian detection, traffic signal detection, one lane tracking and assistant parking.In this work we present an Unmanned Ground Vehicle system through Computer vision specifically Stereoscopic vision, through a sensor we obtain a disparity map that allows us to quantify the depth of each point of the captured image. The system captures an image of its environment that through internal processing of the sensor, returns a disparity map as data, which is processed by an algorithm that allows the system to navigate in a region of the space in which it is positioned for Autonomous Exploration.

Evaluating mobility vs. latency in unmanned ground vehicles

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.

Understanding Human Response to the Presence and Actions of Unmanned Ground Vehicle Systems in Field Environment

The objective of this research was to investigate how humans would respond to unmanned ground vehicles (UGV) operating in their environment. These environments included a shopping mall, two different sports complexes, and a university campus setting. The field study included video observations of 784 pedestrians. Additionally, survey data were collected and matched to 115 pedestrian observations. The field evaluations were conducted over a period of six weeks at the four locations. The results indicate that of the 784 pedestrians observed, 63.5% took time to observe the robot, 30.6% ignored the robot, and 5.9% stopped and interacted with the robot. Based on the individual results from the 115 pedestrians that completed the survey, those who indicated having positive feelings (e.g., strong, comfortable, excited) had an increased likelihood of interacting with the UGV. If the pedestrian had owned a dog they were more likely to interact with the robot. Pedestrians traveling in groups were also more likely to stop and interact with the robot than those traveling alone. The results from this field research will be used in the near future to inform the development of a pedestrian model to include responses to robots that may be in the environment, and could also be used to inform design features for UGVs. As robots continue to become more prevalent in society, it is important to consider how humans will interact with them when encountered.

Online Aerial Terrain Mapping for Ground Robot Navigation.

This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient paths may be generated and then executed. The two vehicles are registered in a common coordinate frame using a real-time kinematic global positioning system (RTK GPS) and all image processing is performed onboard the unmanned aerial vehicle, which minimizes the data exchanged between the vehicles. This paper describes the architecture of the system and quantifies the registration errors between the vehicles.

Supercapacitors in Tandem with Batteries to Prolong the Range of UGV Systems

The purpose of this study was to explore a novel approach to power hybridization in relation to its effectiveness in an unmanned ground vehicle (UGV). This hybridization method is modeled after the power distribution methods found in living organisms, which utilize glycogen stores and adipose tissue to optimize power and energy density strengths and weaknesses. A UGV rover was constructed with an appropriate distribution of power storage elements creating separate power buffers. The primary buffer consisted of a 10 W solar panel array and a 600 F, 5.4 V supercapacitor bank, and the secondary buffer consisted of a 3.7 V 6 Ah lithium-ion battery pack. The primary buffer provided virtually limitless charge cycles with a superior power density juxtaposed with a secondary buffer that provided superior energy density and volumetric versatility. The design of this rover is presented in this paper; it was tested under manual and autonomous modes. The rover was found to be capable of effectively operating solely on the primary power buffer in high to low luminous conditions while being able to carry out basic extravehicular activities. The rover could travel roughly 22 km without any input power on a full charge of both buffers, and could smoothly switch between its own power buffers during operation, all while transmitting live first person video (FPV) and network data. The introduction of control algorithms on the onboard microcontroller unit (MCU) was also explored in both manual and autonomous configurations. The latter integrated linear regression to intelligently manage power and locomotion based on sensory data from photoresistors.

Efficient implementation of the ML estimator for high‐resolution angle estimation in an unmanned ground vehicle

Using a maximum-likelihood (ML) estimator for high-resolution angle estimation is well-suited for automotive radars. It is superior to other estimators in cases of small antenna aperture, small samples, and correlated signals. However, the computing power it requires prevents its widespread implementation. In this study, the authors propose a novel implementation method of the ML estimator that achieves appropriate memory requirements and computational costs by defining new variables. The proposed method is appropriate for practical implementation because calculation of the objective function is based only on independent multiply-add operations and can be easily parallelised. Real measured data from their radar of an unmanned ground vehicle system demonstrates the performance of the proposed method. Experimental results show that the proposed ML estimator is more robust than the conventional MUltiple SIgnal Classification method under conditions of low signal-to-noise ratio and a small number of snapshots.

Blackboard Architecture for Detecting and Notifying Failures for Component-Based Unmanned Systems

This paper describes the blackboard architecture that is capable of detecting the component failures/recoveries in the component-based unmanned systems and notifying them to the associated components. The blackboard architecture monitors each component of the system in order to detect its failures/recoveries at runtime and identify the causes of failures. Using the dependency relationships between components, the blackboard architecture performs impact analysis so that it determines the scope of failure/recovery notification in the components of the system. The notification messages delivered to the components can trigger safety actions against the failures. The prototypes of blackboard architecture have been developed for Microsoft Robotics Developer Studio (MSRDS) based unmanned systems and Robot Operating System (ROS) based unmanned systems. The prototypes are used to validate the blackboard architecture with an unmanned ground vehicle (UGV) system and a patrolling robot system as case studies.

Operator Suspicion and Decision Responses to Cyber-Attacks on Unmanned Ground Vehicle Systems

Cyber-attacks against cyber-physical systems (CPS), such as unmanned vehicles, are emergent threats with potentially catastrophic impacts, and this issue has drawn considerable interest by military agencies. Abundant body of research has attempted to address the physical security aspects of CPS; however, research addressing the human dimensions of cyber-attack detection and responses from an operator and operational perspective is sparse. This research has provided a novel probe into the human factors affecting operator resilience in responding to cyber-attacks, which are situations characterized by uncertainty and malicious intent. The variability of individual operators makes it improbable to grasp the full range of factors contributing to operator performance; however, the application of Suspicion Theory as proposed by Bobko et al. (2013), provides a starting point to aid in understanding operator performance in situations involving malicious intent (e.g. a cyber-attack). According to the theory, malicious intent is a critical component of operator suspicion, which is a key factor in operator response to cyber-attacks. The current research explored this human dimension through scenario-based, human-in-the-loop simulation experiments with Air Force personnel. It included both abstract and empirical assessments of the application of Suspicion Theory to operator detection and responses to cyber-attacks against an unmanned vehicle system, and it took a systems-oriented approach to the problem by considering the interaction of a Human-Machine Team (HMT) in the response. The HMT here refers to an operator and a Sentinel, which is an automated cyber-attack detection aid. The study evaluated the effects of suspicion, as well as the effects of perceived consequence, on the operator, and the resulting HMT quality of performance in responding to alerts, including both false alarms and properly detected cyber-attack scenarios. The findings show that Sentinel alerts alone do not create operator suspicion. Instead, alerts can serve as a catalyst for a wider information search by the operator, which, on a situational basis can lead to formation of increased operator suspicion. The analysis of experimental results pointed to a negative correlation between operator suspicion and performance score that measured the quality of a response to the given scenario. In addition, a strong correlation between HMT performance score and task response time was noted.

Trajectory tracking control system of unmanned ground vehicle

Trajectory tracking control system of unmanned ground vehicle

Enhancing Path Following Control Performance of Autonomous Ground Vehicle through Coordinated Approach under Disturbance Effect

This study proposes the model predictive control (MPC) with proportional-integral (PI) controller for coordination of active front steering (AFS) and direct yaw moment control (DYC) maneuvers for path following control in an unmanned ground vehicle system. A single-track mode of lateral-yaw motions based on a linearized vehicle model with linear tire characteristics is used for controller design, while the vehicle model used includes roll dynamic motion for the double-track model with a nonlinear tire model. Based on a known trajectory, we tested the vehicle at high forward speed due the fact that the roll dynamic motion will be influenced at high vehicle speed for a double lane change scenario in order to follow the desired trajectory as close as possible, while rejecting the effects of a wind gust. We compared two different controllers; i) MPC with PI of an AFS and, ii) MPC with PI for coordination of AFS and DYC. The simulation results show the proposed coordinated control yielded better tracking performance through high speed maneuvers than AFS maneuvers only. It also demonstrates that the proposed control methods are useful to maintain and enhance vehicle stability along the desired path, and has the ability to eliminate the effect of crosswind.

Combined fuzzy approach for online speed planning and control with real vehicle implementation

This paper combines two methods for speed planning and speed control which are based on fuzzy logic. Author's previous studies on speed planning and speed control were designed individually in two papers and only simulation results were illustrated there. This paper concentrates on how to combine and use these two subsystems in a real unmanned ground vehicle (UGV) system. Combined speed planner and speed controller is first simulated with an obstacle avoidance algorithm using a single track vehicle model. After that, this new combined system is compared with a classical planning and control approach. Finally the new method is applied in a real UGV platform. The results of the tests show the real-time applicability of the methods and are very promising for future developments.

3402 最適軌道に対するスキッドステア式小型無人車両の追従制御

This paper investigates an optimal control maneuver of a skid-steer small unmanned ground vehicle (UGV). The optimal control problem is prescribed as a minimum-time maneuver of the UGV, and it is solved using pseudospectral (PS) optimal control theory. The optimal trajectory which includes the pivot-turn maneuver is provided to the real UGV system as reference input commands, and the vehicle tracks it by the gain-scheduled feedback law on the vehicle position error. The results provide nominal tracking performance and validate the feasibility of the approach.

Fused Dynamics of Unmanned Ground Vehicle Systems

Fused Dynamics of Unmanned Ground Vehicle Systems

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system.

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system.