Search Results (12)

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

To improve the accuracy and real-time performance of trajectory tracking control for a four-wheel differential drive intelligent sweeping vehicle, a trajectory tracking control method based on an adaptive strong tracking extended Kalman filter (ASTEKF) state estimator and a Laguerre-based model predictive controller (LMPC) is proposed. Based on the kinematic model of the intelligent sweeping vehicle, an ASTEKF state estimator is designed for vehicle state estimation, and a Laguerre-function-based model predictive controller is developed for trajectory tracking control, thereby enhancing the control accuracy and stability of the vehicle. Simulation results demonstrate that compared with the conventional MPC algorithm, the proposed ASTEKF–LMPC algorithm reduces the maximum lateral error by 44.65% and the maximum heading angle error by 40.96% during sweeping operations, while under normal driving conditions, the maximum lateral error and maximum heading angle error are reduced by 36.27% and 40.03%, respectively. Furthermore, experimental tests conducted on an intelligent sweeping vehicle platform show that the proposed method reduces the maximum lateral error by 34.25% and the maximum heading angle error by 23.18%, thereby validating the effectiveness of the proposed algorithm in intelligent sweeping operations. Full article

Hypersonic morphing vehicles (HMVs), renowned for their adaptive structural reconfiguration and cross-domain maneuverability, confront formidable reentry guidance challenges under multiple no-fly zones, stringent path constraints, and nonlinear dynamics exacerbated by morphing-induced aerodynamic uncertainties. To address these issues, this study proposes a hierarchical framework integrating an A-based energy-optimal waypoint planner, a deep deterministic policy gradient (DDPG)-driven morphing policy network, and a quasi-equilibrium glide condition (QEGC) guidance law with continuous sliding mode control. The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). The QEGC law ensures robust longitudinal-lateral tracking via smooth hyperbolic tangent switching. Simulations demonstrate generalization across diverse targets (terminal errors < 0.24 km) and robustness under Monte Carlo deviations (0.263 ± 0.184 km range, −12.7 ± 42.93 m/s velocity). This work bridges global trajectory planning with real-time morphing adaptation, advancing intelligent HMV control. Future research will extend this framework to ascent/dive phases and optimize its computational efficiency for onboard deployment. Full article

To enhance the precision and real-time performance of trajectory tracking control in differential-steering intelligent sweeping robots and to improve the adaptability of the control algorithm to errors caused by sensor noise, tire slip, and skid, an MPC-PID (Model Predictive Control–Proportional-Integral-Derivative) dual closed-loop control strategy was proposed. This strategy integrates a Kalman filter-based state estimator and a sliding compensation module. Based on the kinematic model of the intelligent sweeping robot, a model predictive controller (MPC) was designed to regulate the vehicle’s pose, while a PID controller was used to adjust the longitudinal speed, forming a dual closed-loop control algorithm. A Kalman filter was employed for state estimation, and a sliding compensation module was introduced to mitigate wheel slip and lateral drift, thereby improving the stability of the control system. Simulation results demonstrated that, compared to traditional MPC control, the maximum lateral deviation, maximum heading angle deviation, and speed response time were reduced by 50.83%, 53.65%, and 7.10%, respectively, during sweeping operations. In normal driving conditions, these parameters were improved by 41.58%, 45.54%, and 24.17%, respectively. Experimental validation on an intelligent sweeper platform demonstrates that the proposed algorithm achieves a 16.48% reduction in maximum lateral deviation and 9.52% faster speed response time compared to traditional MPC, effectively validating its enhanced tracking effectiveness in intelligent cleaning operations. Full article

The development of intelligent task allocation and path planning algorithms for unmanned surface vehicles (USVs) is gaining significant interest, particularly in supporting complex ocean operations. This paper proposes an intelligent hybrid algorithm that combines task allocation and path planning to improve mission efficiency. The algorithm introduces a novel approach based on a self-attention mechanism (SAM) for intelligent task allocation. The key contribution lies in the integration of an adaptive distance field, created using the locking sweeping method (LSM), into the SAM. This integration enables the algorithm to determine the minimum practical sailing distance in obstacle-filled environments. The algorithm efficiently generates task execution sequences in cluttered maritime environments with numerous obstacles. By incorporating a safety parameter, the enhanced SAM algorithm adapts the dimensional influence of obstacles and generates paths that ensure the safety of the USV. The algorithms have been thoroughly evaluated and validated through extensive computer-based simulations, demonstrating their effectiveness in both simulated and practical maritime environments. The results of the simulations verify the algorithm’s capability to optimize task allocation and path planning, leading to improved performance in complex and obstacle-laden scenarios. Full article

In addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes an intelligent morphing decision method based on deep neural networks (DNNs) for the autonomous morphing decision problem of hypersonic boost-glide morphing vehicles under process constraints. Firstly, we established a dynamic model of a hypersonic boost-glide morphing vehicle with a continuously variable sweep angle. Then, in order to address the decision optimality problem considering errors and the heat flux density constraint problem during the gliding process, interference was introduced to the datum trajectory in segments. Subsequently, re-optimization was performed to generate a trajectory sample library, which was used to train an intelligent decision-maker using a DNN. The simulation results demonstrated that, compared with the conventional programmatic morphing approach, the intelligent morphing decision maker could dynamically determine the sweep angle based on the current flight state, leading to improved range while still adhering to the heat flux density constraint. This validates the effectiveness and robustness of the proposed intelligent decision-maker. Full article

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test. Full article

Various sensors are embedded in automobiles to implement intelligent safety technologies such as autonomous driving and front–rear collision avoidance technology. In particular, ultrasonic sensors have been used in the past because they have an accuracy of centimeters to sub-centimeters in air despite their low cost and low hardware complexity. Recently, the crosstalk problem between ultrasonic sensors has been raised because the number of ultrasonic sensors in the unit space has increased as the number of vehicles increases. Various studies have been conducted to solve the crosstalk, but a demodulation error occurs when signals overlap. Therefore, in this paper, we propose a method that is robust to ultrasonic signal overlap, is robust even at shorter code length, and has reduced time of flight (TOF) error compared to the existing method by applying frequency sweep keying modulation based on code division multiple access (CDMA). As a result of the experiment, the code was detected accurately regardless of the overlap ratio of the two signals, and it was robust even in situations where the power of the two signals was different. In addition, it shows an accurate TOF estimation even if the ID code length is shorter than the existing on–off-keying, frequency shift keying, and phase shift keying methods. Full article

The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m³, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety. Full article

For a driving motor stator of EDU (Electric Drive Unit) intelligent electric transmission of a domestic plug-in hybrid electric vehicle, modal tests are performed on the motor stator with or without motor shell. Either the hammering method or the frequency sweeping method is used in the test. The modal frequencies, modal shapes, and damping ratios of the first five orders that meet the requirements of the modal confidence criterion are obtained. The influence of the motor shell on the low-order radial modal of the motor stator is discussed. The results show that similar results are obtained in the modal parameter estimation respectively using the hammering method and the frequency sweeping method. They can both be used for low-order radial modal test of the motor stator. The motor stator without shell exhibits a linear structure in the frequency domain. Each modal frequency obtained by the frequency sweeping method is slightly higher than that obtained by the hammering method. Full article

The localization system of low-cost autonomous vehicles such as autonomous sweeper requires a highly lateral localization accuracy as the vehicle needs to keep a near lateral-distance between the side brush system and the road curb. Existing methods usually rely on a global navigation satellite system that often loses signal in a cluttered environment such as sweeping streets between high buildings and trees. In a GPS-denied environment, map-based methods are often used such as visual and LiDAR odometry systems. Apart from heavy computation costs from feature extractions, they are too expensive to meet the low-price market of the low-cost autonomous vehicles. To address these issues, we propose a mono-vision based lateral localization system of an autonomous sweeper. Our system relies on a fish-eye camera and precisely detects road curbs with a deep curb detection network. Curbs locations are then referred to as straightforward marks to control the lateral motion of the vehicle. With our self-recorded dataset, our curb detection network achieves 93% pixel-level precision. In addition, experiments are performed with an intelligent sweeper to prove the accuracy and robustness of our proposed approach. Results demonstrate that the average lateral distance error and the maximum invalid rate are within 0.035 m and 9.2%, respectively. Full article

Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships’ velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories. Full article