Search

In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader's NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents' state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions. [ABSTRACT FROM AUTHOR]

In a complex and unknown battlefield environment, unmanned vehicle clusters can perform more complex tasks than a single unmanned ground vehicle (UGV). The formation and obstacle avoidance of unmanned vehicle clusters is one of the research hotspots in the field of swarm intelligence. To reduce the deviation between the actual formation position and expected formation when unmanned vehicle clusters avoid obstacles in an unknown environment, this paper proposes a cooperative formation and obstacle avoidance control method for unmanned vehicle clusters based on an improved dynamic window approach (DWA). In addition to the azimuth evaluation, obstacle evaluation, and speed evaluation factors of the path evaluation function in DWA, the direction coordination and formation maintenance factors are added. We established a mathematical model of unmanned vehicle cooperative formation and calculated in real time the expected position, direction, and speed of each following vehicle according to the expected formation. The sum of the deviation between the real driving and expected directions of each following vehicle was taken as the direction cooperation factor, and the sum of the absolute deviation between the real and expected positions of each following vehicle was taken as the formation-keeping factor. When unmanned vehicles approach unknown obstacles, the adaptive collaborative adjustment of the relative position and speed is performed based on the improved DWA, which can improve the precision of the formation positions during the obstacle avoidance process. In this paper, a simulation is conducted using a scenario of three unmanned vehicles forming a triangular formation to avoid obstacles and three obstacles. The simulation results show that the control adjustment based on the improved dynamic window and variable weight optimization algorithm is more timely than the traditional formation control based on the artificial potential field. Additionally, the formation position deviation is relatively small when avoiding obstacles. The average deviation between the actual position of the vehicle formation and the desired position of the formation near the obstacle is used as the evaluation function. The coefficients of the path evaluation function of the improved DWA are optimized based on the variable weight pigeon-inspired optimization (PIO). It can improve the formation, maintaining accuracy in obstacle avoidance. In summary, the proposed improved algorithm can make the unmanned vehicle cluster adjust the driving speed and direction cooperatively in obstacle avoidance. Moreover, it can improve the stability and accuracy of the dynamic formation.

In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader’s NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents’ state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions.

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications. [ABSTRACT FROM AUTHOR]

In this paper, a time-varying delay output feedback control method based on the potential barrier function is proposed, which can solve the communication delay and field-of-view (FOV) constraints of Unmanned Ground Vehicle (UGV) clusters when communicating with ultra-wide-angle cameras. First, a second-order oscillator and an output feedback controller are utilized to feed back the position and direction of neighboring vehicles by exchanging control quantities and to solve the time-varying delay in the position computation of the ultra-wide-angle camera. Due to the limited target radiation range perceived by the camera, an FOV-constrained potential function is adopted to optimize the design of the sliding mode surface. The stability of the closed-loop control system is analyzed by applying the Lyapunov method. Finally, simulation experiments are conducted to verify the effectiveness of the consensus scheme in addressing the communication delay and FOV constraint problem under two different initial conditions. [ABSTRACT FROM AUTHOR]

Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction. [ABSTRACT FROM AUTHOR]

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

Mobile Manipulators (MoMa) is a category of mobile robots designed to assist people with motor disabilities to perform object retrieval tasks using a webcam-based gaze control system. Using off-the-shelf components such as reproducible acrylic and 3D-printed plates, and a webcam for eye tracking, MoMa serves as an inexpensive, open-source, and customizable solution in assistive robotics. The robotic system consists of a mobile base that can move forward and backward, as well as turn in place; and a 2-axis cartesian arm equipped with a claw gripper that opens and closes. The simple movement of the robot also allows for a simple control method and graphical user interface (GUI). The user receives information about what is in front of the robot through a mounted camera, and, by looking at parts of the screen that correspond to controls, has their gaze predicted by a convolutional neural network and sends commands wirelessly. The performance of the entire system has been validated through testing of the gaze prediction model, the integration of the control system, as well as its task completion capabilities. All the design, construction and software files are freely available online under the CC BY 4.0 license at https://doi.org/10.17632/k7yfn6wdv7.2.

Mobile robotic technologies are emerging in agriculture in recent years, driven by labor shortages, the need of precision farm management, and the improvement of farmers' quality of life. Mobile robotic technologies, both aerial and ground, have been considered as one of the most important and promising solutions to address agricultural challenges. Nevertheless, the implementation rate in farms is still low. The aim of this study was to evaluate the performance of a mobile agricultural ground robot to assess its possible implementation in the farm environment. The mobility performances as well as the energy consumption of the mobile robot were evaluated considering different interaction systems (teleoperated, autonomous driving) and application areas (weeding, tilling). The results show that the forward speed of the tested mobile robot is affected by the towed load, up to 230 kg, and that the robot could be driven continuously for 2 h in the field without decreasing its performances at a maximum speed between 0.71–0.77 m s-1. The autonomy of the tested robot is limited, but this is mainly due to the battery technology used. The mobile robot effectively tow implements for weeding and tilling operations managing grass with a single pass (40 % weed removal) and improving soil bulk density. The average energy consumption across the different settings was about 1.43 kWh for each hour of use. Moreover, although the RTK-GNSS autonomous navigation used was a low-cost system, the results showed a good accuracy, which allowed the mobile robot to navigate autonomously in the 2 m inter-row of the vineyard. Finally, the working capacity of the mobile robots, considering the implements used, is on average 0.29 ha h-1. This study provides further evidence for the possible use of ground mobile robot in agriculture to carry out autonomous or semi-autonomous operations.

Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation of a large number of images for training. To address this limitation, we present a method for traversability estimation on unlabeled videos that combines dataset synthesis, self-supervision and unsupervised domain adaptation. We pose the traversability estimation as a vector regression task over vertical bands of the observed frame. The model is pre-trained through self-supervision to reduce the distribution shift between synthetic and real data and encourage shared feature learning. Then, supervised training on synthetic videos is carried out, while employing an unsupervised domain adaptation loss to improve its generalization capabilities on real scenes. Experimental results show that our approach is on par with standard supervised training, and effectively supports robot navigation without the need of manual annotations. Training code and synthetic dataset will be publicly released at: . [ABSTRACT FROM AUTHOR]

A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle-unmanned ground vehicle (UAV-UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity. [ABSTRACT FROM AUTHOR]

The paper presents a study of the performance and development of unmanned ground vehicles (UGVs), establishing mathematical and numerical models of the chassis system. The model analysis is performed by 3D software package SolidWorks 2018 with finite element discretization. The mesh modelling and analysis are focused on studying the strength and stiffness of the robotic platform chassis and the distribution of stress and deformation in the extremal condition. The paper also presents an autopilot design with a new cascade control system for the autonomous motion of an unmanned ground vehicle based on proportional–integral–derivative (PID) and feedforward (FF) control. The PID-FF controller is part of a UGV used in a hybrid control system for precise control and stabilization, which is necessary to increase the vehicle motion stability and maneuver precision. The hybrid PID-FF control system proposed for the ground vehicle model gives satisfactory control quality while maintaining the simplicity of the control system. The presented tests performed in mechanical design and control analysis give good results and prove the usefulness of the designed unmanned device. [ABSTRACT FROM AUTHOR]

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs' performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation. [ABSTRACT FROM AUTHOR]

A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is designed to be affordable and suitable for smaller applications or specific purposes. In this study, the firefly algorithm was utilized to address obstacle avoidance challenges in the presence of dynamic or statically positioned uncertain obstacles. The autonomous vehicle successfully reached the intended destination, demonstrating a satisfactory level of accuracy. Regardless of the starting point, the vehicle arrived at the predetermined position within an area measuring 5 meters in diameter. The achievement of such results can be attributed to the cost-effective selection of sensors, utilization of a simple algorithm, and the implementation of a moderately powered processor and circuit components. [ABSTRACT FROM AUTHOR]

During the operation of six-wheel independent drive unmanned ground vehicle (6WID UGV) in the field, the inconsistent tire contact characteristics and frequent steering maneuvers have led to increasingly prominent issues of tire excessive wear and lateral instability. Therefore, a coordinated hierarchical controller for 6WID UGV is proposed to ensure the stability of the steering process while reducing tire wear and motor energy consumption. First, a sliding mode controller (SMC) with an extended-state observer (ESO) is proposed for lateral stability control referencing the active disturbance rejection control (ADRC) technique, achieving fast response and robustness to uncertainty. Second, the lower controller constructs a Lagrangian function with tire slip energy loss and energy consumption as optimization objectives and allocates torque based on the Pareto optimal solution within the feasible domain given by the upper controller. Finally, the designed controller is tested under typical operating conditions. The results indicate that the designed coordinated controller has significant control performance and economic benefits in improving lateral stability and reducing energy loss. Compared with the torque distribution strategy based on tire vertical load, the tire slip energy is reduced by 44.8 % and 17.7 % in the process of traction/steering and braking/steering, respectively. In addition, the motor energy consumption and the friction braking energy loss are reduced by 12.1 % and 70.8 % respectively. • The hierarchical coordinated control strategy is proposed to maintain the stability with the minimum energy loss. • The extended state observer is used to eliminate the influence of uncertainty. • The torque distribution strategy is proposed to reduce tire slip energy loss and tire wear. [ABSTRACT FROM AUTHOR]

A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle–unmanned ground vehicle (UAV–UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity.

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.

In this paper, a time-varying delay output feedback control method based on the potential barrier function is proposed, which can solve the communication delay and field-of-view (FOV) constraints of Unmanned Ground Vehicle (UGV) clusters when communicating with ultra-wide-angle cameras. First, a second-order oscillator and an output feedback controller are utilized to feed back the position and direction of neighboring vehicles by exchanging control quantities and to solve the time-varying delay in the position computation of the ultra-wide-angle camera. Due to the limited target radiation range perceived by the camera, an FOV-constrained potential function is adopted to optimize the design of the sliding mode surface. The stability of the closed-loop control system is analyzed by applying the Lyapunov method. Finally, simulation experiments are conducted to verify the effectiveness of the consensus scheme in addressing the communication delay and FOV constraint problem under two different initial conditions.

This paper focusses on the path following problem for autonomous Unmanned Ground Vehicles developed for precision agriculture. Such vehicles typically face rough terrain and narrow navigating spaces, so reliability of odometry is largely reduced. In this context, slip estimation/accommodation becomes crucial for localization and navigation of vehicles. In this work, a control architecture is presented coupling MIMO Data-Driven Control and Sliding Mode Control, with the inclusion of a fault estimation mechanism aimed at mitigating the actuators’ loss of effectiveness associated to slipping. The supporting theoretical development makes use of previous results available in the Model Free Adaptive Control framework, which have been extended in order to deal with the path following problem for the vehicle. On the basis of extensive simulations, the proposed control policy is shown able to strengthen the recovery capability and robustness inherently owned by the original adaptive mechanism and control laws.

The novel constructions of hybrid energy sources using polymer electrolyte fuel cells (PEMFCs), and supercapacitors are developed. Studies on the energy demand and peak electrical power of unmanned ground vehicles (UGVs) weighing up to 100 kg were conducted under various conditions. It was found that the average electrical power required does not exceed ~2 kW under all conditions studied. However, under the dynamic electrical load of the electric drive of mobile robots, the short peak power exceeded 2 kW, and the highest current load was in the range of 80–90 A. The electrical performance of a family of PEMFC stacks built in open-cathode mode was determined. A hydrogen-usage control strategy for power generation, cleaning processes, and humidification was analysed. The integration of a PEMFC stack with a bank of supercapacitors makes it possible to mitigate the voltage dips. These occur periodically at short time intervals as a result of short-circuit operation. In the second construction, the recovery of electrical energy dissipated by a short-circuit unit (SCU) was also demonstrated in the integrated PEMFC stack and supercapacitor bank system. The concept of an energy-efficient, mobile, and environmentally friendly hydrogen charging unit has been proposed. It comprises (i) a hydrogen anion exchange membrane electrolyser, (ii) a photovoltaic installation, (iii) a battery storage, (iv) a hydrogen buffer storage in a buffer tank, (v) a hydrogen compression unit, and (vi) composite tanks. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of the potential future use of unmanned ground vehicles for forest inventories, we present the first experiences with SPOT, a legged robot equipped with a LiDAR instrument and several cameras that have been used with a teleoperation approach for single-tree detection and measurements. This first test was carried out using the default LiDAR system (the so-called enhanced autonomy payload-EAP, installed on the board of SPOT to guide autonomous movements) to understand advantages and limitations of this platform to support forest inventory activities. The test was carried out in the Vallombrosa forest (Italy) by assessing different data acquisition methods. The first results showed that EAP LiDAR generated noisy point clouds where only large trees (DBH ≥ 20 cm) could be identified. The results showed that the accuracy in tree identification and DBH measurements were strongly influenced by the path used for data acquisition, with average errors in tree positioning no less than 1.9 m. Despite this, the best methods allowed the correct identification of 97% of large trees. [ABSTRACT FROM AUTHOR]

• Study describes power characteristics of an unmanned tracked vehicle. • Effect of the causative variables on tractive power efficiency was synergetic. • Slip power efficiency linearly dropped with augmentation of payload weight. • 3.35–43.62 % of vehicle motion power wasted inside track-surface interface. • Results are applicable for autonomous transportation of agricultural payloads. Elucidation of power characteristics of an unmanned tracked vehicle for autonomous transportation of agricultural payloads in greenhouse constructions leads to appearance of an applied research in this study. This novel aim has been chosen based on operational requirement of the vehicle. Hence, it can be highlighted that this paper is initiative study described results of power efficiencies (motion, slip, and tractive power efficiencies) of the vehicle. To this aim, various payloads mounted on a trailer (1–5 kN) were towed by the vehicle through diverse drive speeds (0.17–0.5 m/s). Results illuminate that vehicle drive speed and payload weight had consequential contribution to vehicle motion and tractive power efficiencies. While, vehicle slip power efficiency mainly depended on payload weight. Linear regression approximations demonstrate that dual cumulative contributory effect of the drive speed and payload weight on the motion power efficiency (84.98–97.69 %) and tractive power efficiency (78.62–92.92 %) was antagonist and synergetic, respectively. Meanwhile, the slip power efficiency (81.71–98.36 %) linearly dropped with augmentation of payload weight. Resultant slip and motion power inefficiency were associate with vehicle motion power loss in amplitude of 0.39–67.98 and 2.65–14.03 W, respectively. Consequently, tractive power inefficiency was associate with vehicle motion power loss in amplitude of 3.15–82.01 W. This amplitude spotlights that 3.35–43.62 % of vehicle motion power inevitably wasted inside track-surface interface in agricultural towing tasks. Overall, numerical and analytical descriptions of the results as well as practical suggestions provide appropriate guidelines for vehicle supervisor in order to optimize power characteristics. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero‐shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state‐of‐the‐art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis technologies for unmanned ground vehicles are important for ensuring safety and reliability. Due to the complexity and uncertainty of unmanned ground vehicles, it is challenging to realize accurate and fast fault detection and diagnosis. For the purpose of solving the data-driven fault detection and diagnosis problems of unmanned ground vehicles, improving the diagnostic accuracy and shortening the training time, a novel fault detection and diagnosis method is proposed, which is called JITGP-ELM. In the proposed method, a model estimator based on the just-in-time Gaussian process is designed for the online residual generation to cope with the dynamics and nonlinearity of systems. A fault classifier using Extreme Learning Machine is designed for fault identification with residuals extracted by the just-in-time Gaussian process modelling. The proposed method has online adaptability, noise-resistant ability, and high generalization. A field test on a real unmanned ground vehicle's steering-by-wire system demonstrates the effectiveness of the proposed method.

The paper presents a study of the performance and development of unmanned ground vehicles (UGVs), establishing mathematical and numerical models of the chassis system. The model analysis is performed by 3D software package SolidWorks 2018 with finite element discretization. The mesh modelling and analysis are focused on studying the strength and stiffness of the robotic platform chassis and the distribution of stress and deformation in the extremal condition. The paper also presents an autopilot design with a new cascade control system for the autonomous motion of an unmanned ground vehicle based on proportional–integral–derivative (PID) and feedforward (FF) control. The PID-FF controller is part of a UGV used in a hybrid control system for precise control and stabilization, which is necessary to increase the vehicle motion stability and maneuver precision. The hybrid PID-FF control system proposed for the ground vehicle model gives satisfactory control quality while maintaining the simplicity of the control system. The presented tests performed in mechanical design and control analysis give good results and prove the usefulness of the designed unmanned device.

Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. This paper proposes a novel 3D ArcSAR method to address this issue, which combines rotational synthetic aperture radar (SAR) and direction estimation algorithms. The method aims to reconstruct 3D images of 360° scenes and offers distinctive advantages in both azimuthal and altitudinal sensing. Nevertheless, due to the unique structural characteristics of vehicle SAR, it is limited to receiving only a single snapshot signal for 3D sensing. We propose a resolution algorithm based on ArcSAR and the iterative adaptive approach (IAA) to resolve the limitation. Furthermore, the errors in altitude angle estimation of the proposed algorithm and conventional algorithms are analyzed under various conditions, including different target spacing and signal-to-noise ratio (SNR). Finally, we design and implement a prototype of the 3D ArcSAR sensing system, which utilizes a millimeter-wave MIMO radar system and a rotating scanning mechanical system. The experimental results obtained from this prototype effectively validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase the real-time localization accuracy and efficiency of UGVs, and to improve the robustness of UGVs' odometry within industrial parks—thereby addressing issues related to UGVs' motion control discontinuity and odometry drift—this paper proposes a tightly coupled LiDAR-IMU odometry method based on FAST-LIO2, integrating ground constraints and a novel feature extraction method. Additionally, a novel maintenance method of prior maps is proposed. The front-end module acquires the prior pose of the UGV by combining the detection and correction of relocation with point cloud registration. Then, the proposed maintenance method of prior maps is used to hierarchically and partitionally segregate and perform the real-time maintenance of the prior maps. At the back-end, real-time localization is achieved by the proposed tightly coupled LiDAR-IMU odometry that incorporates ground constraints. Furthermore, a feature extraction method based on the bidirectional-projection plane slope difference filter is proposed, enabling efficient and accurate point cloud feature extraction for edge, planar and ground points. Finally, the proposed method is evaluated, using self-collected datasets from industrial parks and the KITTI dataset. Our experimental results demonstrate that, compared to FAST-LIO2 and FAST-LIO2 with the curvature feature extraction method, the proposed method improved the odometry accuracy by 30.19% and 48.24% on the KITTI dataset. The efficiency of odometry was improved by 56.72% and 40.06%. When leveraging prior maps, the UGV achieved centimeter-level localization accuracy. The localization accuracy of the proposed method was improved by 46.367% compared to FAST-LIO2 on self-collected datasets, and the located efficiency was improved by 32.33%. The z-axis-located accuracy of the proposed method reached millimeter-level accuracy. The proposed prior map maintenance method reduced RAM usage by 64% compared to traditional methods. [ABSTRACT FROM AUTHOR]

This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in "All terrain conditions". They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on "All terrain traversability" and "dead zone" monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB), Leaf Scald (LS), Brown Spot (BS), Bacterial Leaf Blight (BLB) and Bakane (BE) which are detected using Convolutional Neural Network (CNN). [ABSTRACT FROM AUTHOR]

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270km h−1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 28ms−2 $28\,ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts. [ABSTRACT FROM AUTHOR]

As intelligent Unmanned Ground Vehicles (UGVs) find broader applications in areas such as transportation and logistics. The fusion of multiple sensors becomes crucial, since it not only amplifies UGV perception in dynamic scenarios but also underpins their autonomous decision-making capabilities. However, many existing methods only focus on single-sensor data, overlooking the multi-sensor data integration, thereby limiting UGV's scalability and adaptability. In this paper, we introduce the Multi-Sensor Collaborative Decision Network (MSCDN) for autonomous multi-sensor fusion policy learning designed specifically for UGVs. MSCDN is dedicated to integrate the data collected by multi-sensors in simulation environment and can be migrate to real environment. Firstly, a simulation environment mirroring real environment is created, using a framework that transfers UGV decision-making from simulated to real environment with deep reinforcement learning. Secondly, MSCDN uses a multi-sensor attention fusion network to adaptively integrate sensor data, refining UGV responses in dynamic settings. Thirdly, MSCDN's efficacy is tested on both simulated and real UGV lane-keeping tasks, showcasing its superior performance in comparative experiments. Compared to baseline methods, MSCDN reduces training steps and achieves a 35.71 % higher success rate and a 37.5 % quicker task completion time, underlining its proficient multi-sensor data fusion capability.

This paper proposes three new dynamic model-based adaptive sliding mode controllers (ASMC) for trajectory tracking control of unmanned ground vehicles (UGV). The controllers combine kinematic and dynamic control to achieve asymptotic stability and finite-time convergence and their stability is verified using the Lyapunov stability theory. The controllers are designed to address the challenges posed by uncertainties in vehicle dynamic parameters and outperform the sliding mode control (SMC) method in trajectory tracking control. The simulation results of the proposed controllers are also discussed in terms of performance metrics. The simulation results show that the controller proposed in rule 1 shows the better performance in terms of trajectory tracking, and then the controllers proposed in rules 2 and 3 have good performances, respectively. In addition, when the simulation results are compared, it is seen that the performance of all three proposed controllers is better than the SMC.

Over the past decade, the utilization of mobile robots in commercial and defense industries has rapidly increased. These robots are purpose-built to perform specific tasks and have proven to be particularly valuable in dangerous environments where human presence may be problematic. However, identifying hazardous areas for workers, soldiers, and emergen- cies during disasters and providing real-time surveillance data remain significant challenges. Conventional approaches, such as manual surveillance and mapping uncharted territories are time-consuming and susceptible to human error. UGVs enable standoff operations, which lowers or eliminates these problems in demanding, and hazardous conditions. This paper discusses the design and development of Rakshak: a modular UGV as a first response mechanism for 360° of real-time surveillance by mapping unknown areas and small- payload-based logistics operations. Teleoperation of the UGV is via radio transmission, a reliable and efficient method of communication. The modular design of the UGV allows for flexibility in adapting to various applications. Data acquisition and transfer to the mobile application are accomplished through Wi-Fi communication.