Unmanned Ground Vehicle System Research Articles

OffRoadSynth Open Dataset for Semantic Segmentation using Synthetic-Data-Based Weight Initialization for Autonomous UGV in Off-Road Environments

This article concerns the issue of image semantic segmentation for the machine vision system of an autonomous Unmanned Ground Vehicle (UGV) moving in an off-road environment. Determining the meaning (semantics) of the areas visible in the recorded image provides a complete understanding of the scene surrounding the autonomous vehicle. It is crucial for the correct determination of a passable route. Nowadays, semantic segmentation is generally solved using convolutional neural networks (CNN), which can take an image as input and output the segmented image. However, proper training of the neural network requires the use of large amounts of data, which becomes problematic in the situation of low availability of large, dedicated image data sets that consider various off-road situations - driving on various types of roads, surrounded by diverse vegetation and in various weather and light conditions. This study introduces a synthetic image dataset called “OffRoadSynth” to address the training data scarcity for off-road scenarios. It has been shown that pre-training the neural network on this synthetic dataset improves image segmentation accuracy compared to other methods, such as random network weight initialization or using larger, generic datasets. Results suggest that using a smaller but domain-dedicated set of synthetic images to initialize network weights for training on the target real-world dataset may be an effective approach to improving semantic segmentation results of images, including those from off-road environments.

Mission Planning of UAVs and UGV for Building Inspection in Rural Area

Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is introduced to carry the UAVs to reach the rural area and also serve as a charging station. In this paper, the mission planning problem for UAVs and UGV systems is focused on, and the goal is to realize an efficient inspection of buildings in a specific rural area. Firstly, the mission planning problem (MPP) involving UGVs and UAVs is described, and an optimization model is established with the objective of minimizing the total UAV operation time, fully considering the impact of UAV operation time and its cruising capability. Subsequently, the locations of parking points are determined based on the information about task points. Finally, a hybrid ant colony optimization-genetic algorithm (ACO-GA) is designed to solve the problem. The update mechanism of ACO is incorporated into the selection operation of GA. At the same time, the GA is improved and the defects that make GA easy to fall into local optimal and ACO have insufficient searching ability are solved. Simulation results demonstrate that the ACO-GA algorithm can obtain reasonable solutions for MPP, and the search capability of the algorithm is enhanced, presenting significant advantages over the original GA and ACO.

Systematic Literature Review on the Implementation of Unmanned Ground Vehicle System (UGVS) for Defense in Indonesia

Systematic Literature Review on the Implementation of Unmanned Ground Vehicle System (UGVS) for Defense in Indonesia

Replay attack detection and mitigation for cyber–physical systems via RADIR algorithm with encryption scheduling

This paper investigates the security state estimation issue facing replay attacks in confidential cyber–physical systems. Smart sensor equipped with a power harvester transmits critical measurements to the remote estimator over wireless networks, in which the transmission modes include encrypted and normal. In order to complete the persistent threats, the adversary launches multi-stage discontinuous replay attacks to compromise the estimation process. In virtue of the designed label detection scheme, we transform attack detection into encryption scheduling optimization under the constraint of sensor energy harvesting and acquire a rigorous relationship between detection probability, attack duration, and encryption ratio. Different from most existing studies only focusing on attack detection, the localization of replay interval measured by detection immediacy is also analyzed. Subsequently, the optimal periodic encryption scheduling strategy is given to maximally determine the attack interval by adopting an optimization-based approach. Further, a complete algorithm of replay attack detection, isolation, and recovery (RADIR) is designed to minimize deterioration from multi-stage attacks, which can guarantee the estimated performance of the system as much as possible. Finally, the unmanned ground vehicle system is applied to validate the theoretical results, where the dataset of replay attacks is obtained by capturing the normal operation data of a moving vehicle. Also, we compare the optimal encryption strategy designed in this paper with general centralized and random scheduling strategies.

Use of unmanned ground vehicle systems in urbanized zones: A study of vector Mosquito surveillance in Kaohsiung.

Dengue fever is a vector-borne disease that has become a serious global public health problem over the past decade. An essential aspect of controlling and preventing mosquito-borne diseases is reduction of mosquito density. Through the process of urbanization, sewers (ditches) have become easy breeding sources of vector mosquitoes. In this study, we, for the first time, used unmanned ground vehicle systems (UGVs) to enter ditches in urban areas to observe vector mosquito ecology. We found traces of vector mosquitoes in ~20.7% of inspected ditches, suggesting that these constitute viable breeding sources of vector mosquitoes in urban areas. We also analyzed the average gravitrap catch of five administrative districts in Kaohsiung city from May to August 2018. The gravitrap indices of Nanzi and Fengshan districts were above the expected average (3.26), indicating that the vector mosquitoes density in these areas is high. Using the UGVs to detect positive ditches within the five districts followed by insecticide application generally yielded good control results. Further improving the high-resolution digital camera and spraying system of the UGVs may be able to effectively and instantly monitor vector mosquitoes and implement spraying controls. This approach may be suitable to solve the complex and difficult task of detecting mosquito breeding sources in urban ditches.

Towards the resilience quantification of (military) unmanned ground vehicles

In the case of Unmanned Ground Vehicles (UGVs), resilience can be an economical, an environmental, but most importantly, a mission-critical question as well: mission failure caused by the lack of resilience in some cases might imply the loss of the UGV, which could lead to human and financial losses and environmental damage. Thus, the aim of this article is to provide a methodology for UGV resilience analysis by introducing a generalizable method that can be applied both for complete UGV systems and subsystems, and leads to resilience quantification. After proposing a specific resilience definition for UGVs, this article proposes a method for UGV resilience assessment using process graphs, created based on the system components and the expected behavior of UGVs. To provide a context for the introduced solution, existing methods applied for UGV resilience assessment are briefly mentioned. The application of the proposed method is showcased on the perception subsystem of a UGV, finalized with the evaluation of the achieved results.

Inconsistent and Incomplete Datasheet: The Case for Systematic Use of Requirement Engineering

ABSTRACTThe lifecycle of a system is extended from its early conception to its retirement of service. The lifespan of unmanned ground vehicles (UGVs) can be expected to last over 50 years in the defense market. In this context, the rising complexity of UGV systems imposes engineering steps that would ensure both capabilities of the system and resilience to its future inclusion in a system‐of system context. During its operational usage, the UGV is supposed to be maneuvered for specifically designed purposes following user manual datasheet of the components off‐the‐shelf (COTS) that were integrated. This paper exposes the public user datasheet relevance compared to the system engineering requirements that are the artifacts of system design architecture. The use of connecting COTS user manual to system requirements is discussed, all the more if the systems are to be re‐used in a system production line. This article is intended to explore system of system conception methods for future robotized battlefield.

Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario

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.

Inconsistent and Incomplete Datasheet: The case for systematic use of requirement engineering

AbstractThe lifecycle of a system is extended from its early conception to its retirement of service. The lifespan of Unmanned Ground Vehicles (UGVs) can be expected to last over 50 years in the defense market. In this context, the rising complexity of UGV systems imposes engineering steps that would ensure both capabilities of the system and resilience to its future inclusion in a system‐of‐system context. During its operational usage, the UGV is supposed to be maneuvered for specifically designed purposes following user manual datasheet of the components off‐the‐shelf (COTS) that were integrated. This paper exposes the public user datasheet relevance compared to the system engineering requirements that are the artifacts of system design architecture. The use of connecting COTS user manual to system requirements is discussed, all the more if the systems are to be re‐used in a system production line. This article is intended to explore system of system conception methods for future robotized battlefield.

Datasheet: where is requirements engineering gone? A case in Benchmarking Mobile Robotics

AbstractThe lifecycle of a system is extended from its early conception to its retirement of service. The lifespan of Unmanned Ground Vehicles (UGVs) can be expected to last over 50 years in the defence market. In this context, the rising complexity of UGV systems imposes engineering steps that would ensure both capabilities of the system and resilience to its future inclusion in a system‐of‐system context. During its operational usage, the UGV is supposed to be manoeuvred for specifically designed purposes following user manual datasheet of the components off‐the‐shelf (COTS) that were integrated. This paper exposes the public user datasheet relevance compared to the system engineering requirements that are the artefacts of system design architecture. The use of connecting COTS user manual to system requirements is discussed, all the more if the systems are to be re‐used in a system production line.

Optimal Unmanned Ground Vehicle—Unmanned Aerial Vehicle Formation-Maintenance Control for Air-Ground Cooperation

This paper investigates the air–ground cooperative time-varying formation-tracking control problem of a heterogeneous cluster system composed of an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Initially, the structure of the UAV–UGV formation-control system is analyzed from the perspective of a cooperative combat system. Next, based on the motion relationship between the UAV–UGV in a relative coordinate system, the relative motion model between them is established, which can clearly reveal the physical meaning of the relative motion process in the UAV–UGV system. Then, under the premise that the control system of the UAG is closed-loop stable, the motion state of the UGV is modeled as an input perturbation. Finally, using a linear quadratic optimal control theory, a UAV–UGV formation-maintenance controller is designed to track the reference trajectory of the UGV based on the UAV–UGV relative motion model. The simulation results demonstrate that the proposed controller can overcome input perturbations, model-constant perturbations, and linearization biases. Moreover, it can achieve fast and stable adjustment and maintenance control of the desired UAV–UGV formation proposed by the cooperative combat mission planning system.

Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments.

During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.

Stabilization of random nonlinear systems subject to deception attacks

AbstractIn this article, the stabilization problem of a class of random networked control nonlinear systems suffering from deception attacks is considered. First, an appropriate attack model described by the random impulsive nonlinear system is developed, and the discrete dynamics have multiple random impulsive intensity, whose type is determined by a Markov chain. Second, the criteria of noise‐to‐state exponential stability in the mth moment are given for the general random impulsive nonlinear systems based on the (mode‐dependent) average impulsive interval. Third, a state‐feedback controller is designed to stabilize the random networked control nonlinear system under deception attacks on the basis of LMIs method. Finally, the feasibility of the proposed theoretical results is verified by the application to the stabilization problem of the network unmanned ground vehicle systems subject to deception attacks and the effectiveness is verified by the simulation results.

Complete stealthiness false data injection attacks against dynamic state estimation in cyber-physical systems

This paper is concerned with the design of complete stealthiness false data injection (FDI) attacks in cyber-physical systems with Kullback-Leiber divergence (KLD) detector. Different from the FDI attacks against KLD detector in the related researches, the FDI attacks with complete stealthiness can completely remove their influences on innovation. Especially, we present the sufficient and necessary design conditions for achieving the attacks. In addition, the realization method of data-driven attacks based on subspace identification is introduced when the attacker cannot acquire system parameters. And a DC motor system and an unmanned ground vehicle system are employed to verify the effectiveness of our main results.

Towards Multi-Modal Perception-Based Navigation: A Deep Reinforcement Learning Method

In this letter, we present a novel navigation system of unmanned ground vehicle (UGV) for local path planning based on deep reinforcement learning. The navigation system decouples perception from control and takes advantage of multi-modal perception for a reliable online interaction with the surrounding environment of the UGV, which enables a direct policy learning for generating flexible actions to avoid collisions with obstacles in the navigation. By replacing the raw RGB images with their semantic segmentation maps as the input and applying a multi-modal fusion scheme, our system trained only in simulation can handle real-world scenes containing dynamic obstacles such as vehicles and pedestrians. We also introduce a modal separation learning to accelerate the training and further boost the performance. Extensive experiments demonstrate that our method closes the gap between simulated and real environments, exhibiting the superiority over state-of-the-art approaches. Please refer to https://vsislab.github.io/mmpbnv1/ for the supplementary video demonstration of UGV navigation in both simulated and real-world environments.

Adaptive event based predictive lateral following control for unmanned ground vehicle system

AbstractIn this article, the lateral path following problem of unmanned ground vehicle with bounded disturbances is studied. A dual‐mode model predictive control (MPC) algorithm based on adaptive event triggered mechanism is proposed. Compared with the existing MPC algorithm, adaptive event triggered model predictive control (AEMPC) can ensure the tracking accuracy and reduce the optimization computation. Feasibility and stability of AEMPC algorithm are guaranteed by constraint region parameter design. Finally, simulation results of the vehicle dynamics model are given, and the comparisons are made.

Fault-Tolerant Formation Tracking Control for Heterogeneous Multiagent Systems with Directed Topology

In this paper, the adaptive fault-tolerant formation tracking control problem for a set of heterogeneous unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems with actuator loss of effectiveness faults is investigated. The cooperative fault-tolerant formation control strategy for UAV and UGV collaborative systems is classified into the altitude consensus control scheme for follower UAVs and the position cooperative formation control scheme for all followers. The altitude consensus control algorithm is designed by utilizing backstepping control technique to drive all UAVs to a desired predefined height. Then, based on synchronization formation error information, the position cooperative formation control algorithm is proposed for all followers to reach the expected position and perform the desired formation configuration. The adaptive fault estimation term is adopted in the designed fault-tolerant formation control algorithm to compensate for the actuator loss of effectiveness fault. Finally, a simulation example is proposed to reveal the validity of the designed cooperative formation tracking control scheme.

Secure control and proportional-integral-derivative performance of cyber-physical systems with sparse adversarial attacks

This study investigates the secure control problem for discrete-time linear systems with sparse adversarial attacks. The adversarial attacker is assumed to have limited resources and the capability of manipulating a certain number of communication channels between the remote controller and the actuators. For the designer, which channels are attacked and which are not are unknown. A novel secure remote control method is established in this study. This method consists of a control law, a switching function, and a selection mechanism. The selection mechanism is designed to help select a proper feedback gain for the control law and to produce a switching function that prevents attack signals from entering the plant. Under basic and necessary assumptions, the theoretical analysis shows that the secure control problem can be transformed into a state feedback stabilization problem and that the resulting closed-loop system is stable and resilient to proportional-integral-derivative attacks. Simulation on an unmanned ground vehicle system is performed to verify the theoretical results.

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.