This article presents a simple control method for the fully automatic parking of an unmanned vehicle. This method is based on the switching control algorithm and backstepping theory. The fully ...automatic parking is hard to accomplish since it cannot converge to a specified norm, guarantee the convergence rate, and large uncertainties. Global exponential convergence to any prescribed norm bound (<inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>-convergence) is guaranteed, and the convergence rate is explicitly given. For the design of steering laws, a method based on backstepping is proposed and analyzed in detail. The backstepping-based design of a multichain system is obtained. Moreover, an exponentially <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>-convergent control algorithm is adopted to guarantee both converge norm and convergence rate. Real road experiment results are presented and the results show the effectiveness of the control strategies.
This article presents a trajectory following control solution for the lateral motion of an unmanned vehicle. The proposed solution is based on model predictive lateral control. The lateral motion is ...hard to control since it is nonlinear with large dynamics and uncertainties. By making a small angle approximation, the dynamic model can be linearized. A new bounded equivalent function based on the vehicle kinematic model and the Taylor series expansion is presented for trajectory following control solution for the lateral motion problem. The model predictive lateral control is used to ensure both strong robustness and control accuracy. Experiment results in a real environment are presented to show the effectiveness of the proposed method.
In this article, a fast chance-constrained trajectory generation strategy incorporating convex optimization and convex approximation of chance constraints is designed so as to solve the unmanned ...vehicle path planning problem. A path-length-optimal unmanned vehicle trajectory optimization model is constructed with the consideration of the pitch angle constraint, the curvature radius constraint, the probabilistic control actuation constraint, and the probabilistic collision avoidance constraint. Subsequently, convexification technique is introduced to convert the nonlinear problem formulation into a convex form. To deal with the probabilistic constraints in the optimization model, convex approximation techniques are introduced such that the probabilistic constraints are replaced by deterministic ones while simultaneously preserving the convexity of the optimization model. Numerical results, obtained from a number of case studies, validate the effectiveness and reliability of the proposed approach. A number of comparative studies were also performed. The results confirm that the proposed design is able to produce more optimal flight paths and achieve enhanced computational performance than other chance-constrained optimization approaches investigated in this article.
This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by ...interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.
In this paper, we propose an intelligent contactless delivery scheme for unmanned vehicles based on multi-source sensing signals. Our objective is to address the problem of delivering items in ...special scenarios that require contactless delivery, such as hospital isolation areas and isolated hotels. Specifically, we apply the simultaneous localization and map construction (SLAM) algorithm to solve the localization and map construction problem when the unmanned vehicle moves in an unknown environment. Additionally, we improve the open-source Cartographer algorithm and use sensors such as LiDAR and depth camera to achieve localization, multi-terrain map construction, automatic navigation, and obstacle avoidance.
We experimentally test the map construction, navigation, and obstacle avoidance capabilities of the smart cart in specific scenarios. Our results demonstrate that our proposed smart delivery solution works effectively in urban indoor and outdoor scenarios and can be applied to practical contactless delivery scenarios. This new non-contact delivery mode is expected to be a driving force for future development.
The exploration of unknown environment for mobile unmanned vehicles has always been a hot and difficult research topic. Localization and map construction (SLAM) and path planning scheme are the key ...technologies for autonomous localization and navigation of unmanned vehicles. Path planning of mobile unmanned vehicles is one of the most important core technologies of unmanned vehicles, and the planned path should be as optimal and conflict-free as possible. This paper analyzes and compares the global path planning techniques based on Dijkstra and A star. A star algorithm introduces heuristic functions and reduces the search of nodes, which not only retains the advantages of Dijkstra algorithm in obtaining the shortest path, but also takes into account the advantages of breadth first search. Therefore, A star algorithm is selected as the global path planning algorithm in this topic.
Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged ...as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations.
Display omitted
•Unmanned vehicle-based remote water quality monitoring technologies were reviewed.•Types of the unmanned vehicles and their application ranges were investigated.•Unmanned vehicle mountable water samplers and sensors were categorized.•Processing algorithms were analyzed to convert the image data to water quality data.•Future research directions were suggested by analyzing current development status.
Most conventional path generation algorithms search for an optimal path that avoids collisions with obstacles under the constraint of platforms' kino-dynamics. These conventional algorithms usually ...assume that a user position and obstacle locations are accurately known at any point in navigation environments. However, in a positioning network, the accuracy of a position estimate varies depending on, e.g., the ranging accuracy, network geometry, multipath error, and signal blockages which may lead to unexpected situations, including collision and low efficiency path planning. Therefore, positioning accuracy must be considered in path generation to ensure a reliable navigation capability and collision avoidance. To consider positioning accuracy in path planning, the proposed method in this paper uses a mixture of potential and positioning risk fields that generates a hybrid directional flow to guide an unmanned vehicle (UV) in a safe and efficient path. The results of simulations showed that the proposed method generated successful paths for around 90% percent of the tested routes, while using only the potential field method failed for around 50%. To demonstrate the effectiveness of the proposed local hybrid path planning method, we perform an experiment using a small-size quadcopter, and the results are analyzed and discussed.
This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for ...battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy.
Abstract
Acoustic stealth performance is an important performance index of Underwater Unmanned Aerial Vehicles. Due to equipment installation and other reasons, there will be a sea port on the ...bulkhead of the aircraft. Because the sea port destroys the flow field on the surface of the aircraft and produces noise, which affects the underwater communication of the aircraft. In this paper, the numerical simulation method is used to analyze the flow induced noise characteristics of the bulkhead of an underwater unmanned vehicle, and the main noise frequency characteristics at the bulkhead of the estuary are obtained, which provides data reference for the structural design of the estuary and the layout of the underwater communication system.
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