Control of Solar Energy Systems

The unmanned ground vehicle is supposed to replace humans for various applications in both civilian and military area, including transport, delivery, shuttle, clean, patrol, scout, and battle. It has been widely expected that the unmanned ground vehicle will greatly change the human life and the land combat form in the near future. The key techniques of the unmanned ground vehicle are environment perception, motion planning, chassis dynamics control, and cloud control. In this paper, the state of the art of the development of the military and civilian unmanned ground vehicle is introduced, and many classic ones are reviewed in detail in different application categories. The overall design and X-by-wire dynamics control technique of the civilian and military unmanned ground vehicle are reviewed. The X-by-wire technique plays an important role in improving the maneuverability, mobility, and handling performance of the unmanned ground vehicle. Therefore, the chassis dynamics control is considered as a core technique of the X-by-wire unmanned ground vehicle. In addition, as a research trend of the unmanned ground vehicle dynamics control, the cloud-based dynamics control approach of future unmanned ground vehicle based on big data and cloud control techniques is described according to the authors’ work.

Morocco is a country with a lack of fossil fuel resources and an increasing demand for energy. This inspired the country to increase the use of renewable energy in the energy mix. The objective of this study was to conduct an optimization and techno-economic appraisal of a concentrated solar power plant (CSP) using different scenarios that took Ouarzazate city in the south of Morocco as a case study. To achieve this, several parameters were assessed, including the impacts of solar collector assemblies (SCAs), receiver types, heat transfer fluids, cooling systems, solar multiples, and thermal storage hours, with regard to the profitability of the CSP plant. Then, performance and sensitivity analyses were conducted to select the best integration scenarios based on different economic indicators, including levelized cost of electricity (LCOE) and net present value (NPV). The findings revealed that the use of the Luz LS-3 as the collector/SCA, Solel UVAC 3 as receiver, and Dowtherm Q as heat transfer fluid exhibited the highest performance in terms of the annual energy production yield and capacity factor, as well as the lowest real and nominal LCOEs with a wet cooled condenser. Furthermore, the LCOE is extremely sensitive to changes in the number of hours of storage and the solar multiple, and the optimal real and nominal LCOEs are determined by a highly specific combination of the solar multiple and the number of hours of storage. As a consequence, the maximum and minimum net electricity outputs for the best configuration of the Parabolic Trough Collector (PTC) plant were 24.6 GWh and 7.4 GWh in May and December, respectively. Likewise, the capacity factor and the gross-to-net conversion factor for the optimized plant were found to be 47.9%, and 93.5%, respectively. Concerning the economic study, the expected energy cost was 0.1303 USD per kWh and the NPV value for Ouarzazate city was positive (more than USD 20 million), which indicates that the studied PTC plant was estimated to be financially and economically feasible. The results of this analysis are highly significant and may persuade decision makers, financiers, and solar energy industry players to increase their investments in the Kingdom of Morocco.

The use of unmanned ground vehicles (mobile robots) and unmanned aerial vehicles (drones) in the civil infrastructure asset management sector: Applications, robotic platforms, sensors, and algorithms

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

This paper presents a project developed at the University of Alabama at Birmingham (UAB) aimed to design, implement, and test an off-road Unmanned Ground Vehicle (UGV) with individually controlled four drive wheels that operate in stochastic terrain conditions. An all-wheel drive off-road UGV equipped with individual electric dc motors for each wheel offers tremendous potential to control the torque delivered to each individual wheel in order to maximize UGV slip efficiency by minimizing slip power losses. As previous studies showed, this can be achieved by maintaining all drive wheels slippages the same. Utilizing this approach, an analytical method to control angular velocities of all wheels was developed to provide the same slippages of the four wheels. This model-based method was implemented in an inverse dynamics-based control algorithm of the UGV to overcome stochastic terrain conditions and minimize wheel slip power losses and maintain a given velocity profile. In this paper, mechanical and electrical components and control algorithm of the UGV are described in order to achieve the objective. Optical encoders built-in each dc motor are used to measure the actual angular velocity of each wheel. A fifth wheel rotary encoder sensor is attached to the chassis to measure the distance travel and estimate the longitudinal velocity of the UGV. In addition, the UGV is equipped with four electric current sensors to measure the current draw from each dc motor at various load conditions. Four motor drivers are used to control the dc motors using National Instruments single-board RIO controller. Moreover, power system diagrams and controller pinout connections are presented in detail and thus explain how all these components are integrated in a mechatronic system. The inverse dynamics control algorithm is implemented in real-time to control each dc motors individually. The integrated mechatronics system is distinguished by its robustness to stochastic external disturbances as shown in the previous papers. It also shows a promising adaptability to disturbances in wheel load torques and changes in stochastic terrain properties. The proposed approach, modeling and hardware implementation opens up a new way to the optimization and control of both unmanned ground vehicle dynamics and vehicle energy efficiency by optimizing and controlling individual power distribution to the drive wheels.

Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.

As the research hotspot of intelligent transportation system (ITS), unmanned ground vehicle (UGV) with high intelligence is able to reduce the probability of traffic accidents and traffic congestion, and improve the efficiency of urban transportation system. Environmental perception is one of the key research topics in the research of key technologies of UGV. The campus transportation environment, as a simple version of the urban transportation environment, needs to perceive as much traffic information as the urban traffic environment. At the same time, the geographic information system (GIS) has capabilities of strong geographical information storage and management, which can play a critical role in the environmental perception of UGV. In this paper, we study the autonomous driving of unmanned ground vehicle in the campus traffic environment, and the construction method of GIS database is proposed for the perception demand of UGV in the traffic environment of campus. Then, according to the GIS database, autonomous navigation control of UGV is carried out to verify its applicability and effectiveness. After testing, the GIS database proposed in this paper can assist the autonomous driving of UGV well and complete the driving tasks in the campus environment.

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks.

Over the past few decades, the usage of unmanned vehicles has grown exponentially, expanding into applications such as the automation of industrial processes and automobiles. However, their utility has often been limited by operational concerns. Fully controlled unmanned vehicles require multiple human operators, while their fully autonomous counterparts lack the ability to handle the complex maneuvers necessary in natural disaster relief and/or search and rescue situations. Semi-autonomous UAVs offer a feasible compromise between the two extremes. In this scenario, an unmanned aerial vehicle (UAV) sends birds-eye images of the terrain beneath it to a computing cluster, which will identify easily traversable terrain and generate a path of least risk to an unmanned ground vehicle (UGV). If the path's risk is below a certain threshold, then the UGV will be permitted to proceed on its own. Otherwise, a human operator will be notified, so that he or she may control the UGV directly until it exits the most dangerous terrain. This paradigm allows a single operator to manage several UAVs simultaneously.

Radar sensors have received more and more interest for Unmanned Ground Vehicle (UGV) to sense positive and negative obstacles in unstructured environments or out fields. We have proposed a forward-looking InSAR can provide more information of harsh off-road environments than existing UGV based radars. The forward-looking InSAR can provide a scattering image, a coherence image and a digital terrain map of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of obstacle. Those images could provide abundant information for autonomous navigation systems, which would consequently increase the survivability of the UGV on the battlefield. We have conducted a field experiment on a ground covered by sparse grasses to support the phenomenological studies of the backscatter from positive and negative obstacles for autonomous UGV navigation.

With the recent proliferation of unmanned aerial vehicles for geophysical surveying, a novel opportunity exists to develop unmanned ground vehicles in parallel. This contribution features a study to integrate the Husky A200 robotic development platform with a GSMP 35U magnetometer that has recently been developed for the unmanned aerial vehicle market. Methods to identify and reduce the impact of magnetically noisy components on the unmanned ground vehicle platforms are discussed. The noise generated by the platform in laboratory and gentle field conditions, estimated using the fourth difference method for a magnetometer–vehicle separation distance of 121 cm and rotation of the chassis wheels at full speed (1 m/s), is ±1.97 nT. The integrated unmanned ground vehicle was used to conduct two robotic magnetic surveys to map cultural targets and natural variations of the magnetic field. In realistic field conditions, at a full speed of 1 m/s, the unmanned ground vehicle measured total magnetic intensity over a range of 1730 nT at 0.1 m spatial resolution with a productivity of 2651 line metres per hour.

Ground-Air coordination is a very complex environment for a machine learning algorithm. We focus on the case where an Unmanned Aerial Vehicle (UAV) needs to support a group of Unmanned Ground Vehicles (UGVs). The UAV is required to broadcast an image that contains all UGVs, thus, offering a bird-eye-view on the group as a whole. The source of complexity in this task is twofold. First, coordination needs to occur without communication between the UAV and UGVs. Second, the ability of the UAV to sense the UGVs is coupled with the ability of the UAV to learn how to track laterally the UGVs and adapt its vertical position so that the images of the UGVs are appropriately spaced within the camera field of view. In this paper, we propose using the Deep Actor Network component of an Actor-Critic Deep Reinforcement Learning architecture as a supervised learner. The advantage of this approach is that it offers a step towards autonomous learning whereby the full Actor-Critic model can be utilized in the future. Human demonstrations are collected for the deep Actor network to learn from. The system is built using the Gazebo Simulator, Robot Operating System, and the OpenAI Gym. We show that the proposed setup is able to train the UAV to follow the UGVs while maintaining all UGVs within camera range in situations where UGVs are performing complex maneuvers.

As a new carrier to carry out intelligent task, the unmanned ground vehicle (UGV) is an important component of the intelligent transportation system (ITS) in the future. Most of traditional UGV path tracking control methods are deployed in the edge-side (on-board computing platform) with restricted data, which severely limits the improvement of UGVs’ path tracking performance. Therefore, this paper proposes a novel cloud-edge combined control system with MPC parameter optimization based on cloud brain control center (CBCC) applied in future ITS, which consists of edge-side control module and cloud-side optimization module. The proposed control system can optimize control parameters iteratively in CBCC by making the utmost of big data generated by UGVs to markedly enhance the control effectiveness. In CBCC, the path tracking performance is quantitatively evaluated from the aspects of path tracking accuracy, vehicle stability, and control stability. Also, an optimization algorithm is established by using SVR theory. Based on this, the cloud-side optimization module optimizes the control parameters of the edge-side control module by collecting and processing the big data generated by UGVs while driving. Besides, the designed edge-side control module is based on MPC algorithm and innovatively introduces a compensator which obviously reduces the error caused by the inaccuracy of the prediction model. To verify the effectiveness of the proposed cloud-edge combined control system, numerous simulation experiments are carried out. The results shows that the proposed cloud-edge combined control system can improve UGV’s path tracking performance and have strong robustness at different speeds.

With the advancement of artificial intelligence technology, unmanned vehicle (UV) systems, including unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV), have been appearing in many scenarios with wide-ranging applications. It is a crucial factor for enhancing human-computer collaboration efficiency to analyze the impact of unmanned vehicle system combinations on operators. The research in this paper presents the effects of different combinations of UAVs and UGVs (1UAV + 1UGV, 1UAV + 2UGVs, 2UAVs + 1UGV, 2UAVs + 2UGVs) on searching performance and operator’s mental workload for accomplishing search tasks. Completion times for tasks and subjective data with operators (N=16) were collected by using Psychopy and questionnaires, respectively. The results in this research indicate that binary growth of controllable unmanned vehicles doesn’t improve the UV utilization rate, but costing longer completion time and increasing operator’s mental workload. Since an excessive number of unmanned vehicles could have a negative impact on task performance, the insights in the paper are helpful for the design of unmanned vehicle systems and the research of human-computer collaboration.

Unmanned systems, and specifically Unmanned Ground Vehicles, have become a crucial element in conducting combat operations during Ukraine's war for independence against the russian aggressor. A fairly wide range of tasks is assigned to them, based on their purpose: reconnaissance, strike (fire support), logistics, evacuation, and others. The article provides an analysis of the most common multifunctional Unmanned Ground Vehicle models of various types, including those officially codified, currently in use by the Armed Forces of Ukraine. The potential and limitations of Unmanned Ground Vehicles are outlined based on an analysis of their tactical and technical characteristics and combat capabilities drawn from operational experience. The key performance indicator for evaluating the effectiveness of a Unmanned Ground Vehicle unit's application is defined and substantiated. This indicator incorporates group metrics: functionality / combat capabilities, maneuverability, protection (survivability), and autonomy. The interpretation of the chosen components is also commented upon. A developed methodology for evaluating the effectiveness of Unmanned Ground Vehicle unit application is presented. This methodology is based on a multi-criteria evaluation method using a group of indicators with weighting coefficients, and the main criterion is defined as a quantitative (numerical) performance indicator. The stages for evaluating the effectiveness of combat Unmanned Ground Vehicle application using the developed methodology are provided. An evaluation of the effectiveness of a combat Unmanned Ground Vehicle in performing its assigned tasks is conducted, and an illustrative example is provided for clarity.