Search Results (1,472)

Collision avoidance algorithms play a crucial role in ensuring the safety and effectiveness of autonomous ships, which require comprehensive testing in realistic multi-ship encounter scenarios. However, existing scenario generation methods often inadequately represent the spatiotemporal complexity and dynamic risk interactions of real-world encounters, leading to biased evaluations. To bridge this gap, this paper proposes a combinatorial-testing-based scenario generation framework integrated with spatiotemporal complexity optimisation. First, a full-process scenario representation model is developed by abstracting real-world navigation features into a discretised parameter space. Subsequently, a combinatorial-testing-based scenario generation method is adopted to cover the parameter space, generating a high-coverage scenario set. Finally, spatiotemporal complexity is introduced to filter out oversimplified scenarios and extremely dangerous scenarios. Experiments demonstrated that 13.7% of generated scenarios were eliminated as unrealistic or trivial, while high-risk encounter scenarios and multi-ship interaction scenarios were amplified by 7.96 times and 5.84 times, respectively. Compared to conventional methods, the optimised scenario set exhibited superior alignment with real-world complexity, including dynamic risk escalation and multi-ship coordination challenges. The proposed framework not only advances scenario generation methodology through its integration of combinatorial testing and complexity-driven optimisation, but also provides a practical tool for rigorously validating autonomous ship safety systems. Full article

This paper studies the distributed formation control problem for multiple unmanned aerial vehicles (UAVs), focusing on preserving connectivity and avoiding obstacles within the constraints of a limited communication distance and in the presence of multiple dynamic obstacles. The UAV network is modeled as a proximity graph, where the edges are defined by the distances between the UAVs. A hierarchical control strategy is employed to manage the position and attitude subsystems independently. A distributed position formation controller is developed for the position subsystems, utilizing bounded artificial potential functions to preserve the network connectivity and avoid collisions between UAVs while achieving the desired formation. The position controller also integrates a time-varying sliding manifold and obstacle avoidance potential functions to prevent collisions with dynamic obstacles. Additionally, an attitude controller is designed for the attitude subsystem to track the desired attitude angles generated by the positioning subsystem. Numerical simulations validate that the proposed controllers effectively preserve the communication network’s connectivity, avoid collisions between the UAVs and dynamic obstacles, and achieve the desired formation simultaneously. Full article

Multi-robot systems (MRSs) are widely applied in the fields of joint search and rescue, exploration, and carrying. To achieve cooperative tasks and guarantee physical safety, the robots should avoid inter-robot collisions as well as robot–obstacle collisions. However, the collision/obstacle avoidance task usually conflicts with the given cooperative task, which poses a significant challenge for the achievement of multi-robot cooperative tasks. This paper provides a review of the state-of-the-art results in the collision/obstacle avoidance cooperative control of MRSs. Specifically, the latest developments of collision/obstacle avoidance cooperative control are summarized according to different planning strategies and classified into three categories: (1) offline planning; (2) receding horizon planning; and (3) reactive control. Furthermore, specific design solutions for existing reference/command governors are highlighted to demonstrate the latest research advances. Finally, several challenging issues are discussed to guide future research. Full article

Unmanned aerial vehicles (UAVs) offer an efficient solution for power grid maintenance, but collision avoidance during return flights is challenged by crossing power lines, especially for small drones with limited computational resources. Conventional visual systems struggle to detect thin, intricate power lines, which are often overlooked or misinterpreted. While deep learning methods have improved static power line detection in images, they still struggle with dynamic scenarios where collision risks are not detected in real time. Inspired by the hypothesis that the Lobula Giant Movement Detector (LGMD) distinguishes sparse and incoherent motion in the background by detecting continuous and clustered motion contours of the looming object, we propose a Scale-Invariant Looming Detector (SILD). SILD detects motion by preprocessing video frames, enhances motion regions using attention masks, and simulates biological arousal to recognize looming threats while suppressing noise. It also predicts impending collisions during high-speed flight and overcomes the limitations of motion vision to ensure consistent sensitivity to looming objects at different scales. We compare SILD with existing static power line detection techniques, including the Hough transform and D-LinkNet with a dilated convolution-based encoder–decoder architecture. Our results show that SILD strikes an effective balance between detection accuracy and real-time processing efficiency. It is well suited for UAV-based power line detection, where high precision and low-latency performance are essential. Furthermore, we evaluated the performance of the model under various conditions and successfully deployed it on a UAV-embedded board for collision avoidance testing at power lines. This approach provides a novel perspective for UAV obstacle avoidance in power line scenarios. Full article

We propose dependency reduction techniques for the performance enhancement of the Hyperledger Fabric blockchain. A dependency hazard may result from the parallelism in Hyperledger Fabric, which executes multiple transactions simultaneously in a single block. Since multiple transactions in a block are executed in parallel for throughput enhancement, dependency problems may arise among transactions involving the same key (If Z = A + D is executed in parallel with A = B + C, a read-after-write hazard for A will occur). To address these issues, our scheme proposes a transaction dependency checking system that integrates a dependency-tree-based management approach to dynamically prioritize transactions based on factors such as the tree level, arrival time, and starvation possibility. Our scheme constructs a dependency tree for transactions in a block to be executed in parallel over multiple execution units. We rearrange the transactions into blocks in such a way that the dependency among the transactions are removed as far as possible. This allows parallel execution of transactions to be performed without collision, enhancing the throughput against the conventional implementation of Hyperledger Fabric. Our illustrative implementation of the proposed scheme in a testbed for trading renewable energy shows a performance improvement as big as 27%, depending on the input mixture of transactions. A key innovation is the introduction of the Starve-Avoid method, which mitigates data starvation by dynamically adjusting the transaction priorities to balance throughput and fairness, ensuring that no transaction experiences indefinite delays. Unlike existing approaches that require structural modifications to the conventional Hyperledger Fabric, the proposed scheme optimizes the performance as an independent module, maintaining compatibility with the conventional Hyperledger Fabric architecture. Full article

This paper addresses the autonomous rendezvous and docking between a chaser spacecraft and a target spacecraft. An optimal control method is employed to plan the rendezvous and docking maneuver, considering various constraints, including force, velocity, field of view, and collision avoidance with a diamond-shaped obstacle. The optimal trajectories are derived using a symplectic algorithm, which ensures high accuracy and enhances computational efficiency. These trajectories serve as the reference for the maneuver. A PD-based tracking control method is proposed to enable real-time feedback control. An air-bearing experimental system, encompassing state measurement, data transmission, and processing, is established to conduct ground-based tracking experiments. Furthermore, specialized simulators for the chaser and target spacecraft, equipped with a docking mechanism, are designed. Experimental results validate both the feasibility of the reference trajectories and the effectiveness of the PD tracking control approach. Full article

In practical applications of droplet actuation using digital microfluidic (DMF) systems based on electrowetting-on-dielectric (EWOD), various electrode failures can still arise due to diverse operational conditions. To improve droplet transport efficiency, this study proposes a heuristic-elite genetic algorithm (HEGA) for droplet path planning. We introduce a heuristic method and a bidirectional elite fragment recombination method to address the challenge of poor initialization quality in genetic algorithms, particularly in complex environments. These approaches aim to enhance the global search capability and accelerate the algorithm’s convergence. Simulations were performed using MATLAB, and the results indicate that compared to the basic ant colony algorithm, the proposed method reduces the average number of turning points by approximately 17.23% and the average search time by about 92.60%. In multi-droplet transport applications, the algorithm generates optimal paths for test droplets while maintaining fast convergence. Additionally, it effectively prevents droplets from accidentally contacting or merging in non-synthesis areas, ensuring improved testing outcomes for the chip. Full article

Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at O(n²)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach. Full article

For the guidance problem of trajectory tracking in multiple unmanned surface vehicles (USVs), a trajectory tracking guidance method with collision avoidance based on a novel guiding vector field is proposed. Firstly, within the framework of the virtual leader–follower method for formation control, a tracking error model for followers is developed based on the motion model of USVs. Secondly, considering the limitations of conventional trajectory tracking guidance methods in addressing various initial error conditions, a novel guiding vector field is developed for the design of the heading guidance law to enhance tracking performance. Then, a multi-USV collision avoidance strategy is proposed for formation navigation safety. The trigger conditions, actions and release conditions for collision avoidance are established in this strategy. USVs could avoid collision in time by following the commands outlined in the strategy, especially in complex situations where multiple USVs are simultaneously at risk of colliding with each other. And the theoretical proof is completed. Furthermore, the heading and velocity guidance laws are designed by combining the guidance vector field and the collision avoidance strategy. It is demonstrated that the tracking errors of the system are uniformly bounded based on Lyapunov stability theory. Finally, the effectiveness of the method is verified through simulation. Full article

Utilizing fully distributed intelligent control algorithms has enabled the gradual adoption of the multiple unmanned aerial vehicles system for executing Target Tracking and Encirclement missions in industrial and civil applications. Restricted by the evasion behavior of the target, current studies focus on constructing zero-sum game settings, and existing strategy solvers that accommodate continuous state-action spaces have exhibited only modest performance. To tackle the challenges mentioned above, we devise a Stochastic Potential Game framework to model the mission scenario while considering the environment’s limited observability. Furthermore, a multi-agent reinforcement learning method is proposed to estimate the near Nash Equilibrium strategy in the above game scenario, which utilizes time-serial relative kinematic information and obstacle observation. In addition, considering collision avoidance and cooperative tracking, several techniques, such as novel reward functions and recurrent network structures, are presented to optimize the training process. The results of numerical simulations demonstrate that the proposed method exhibits superior search capability for Nash strategies. Moreover, through dynamic virtual experiments conducted with speed and attitude controllers, it has been shown that well-trained actors can effectively act as practical navigators for the real-time swarm control. Full article

Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming (CLF-CBF-QP) approach. This framework enables a vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of simulations were conducted, starting with basic model-in-the-loop (MIL) non-real-time simulations, followed by real-time simulations. Multiple scenarios with different controller configurations and obstacle setups were tested, demonstrating the effectiveness of the proposed controllers in avoiding collisions. Real-time simulations in Simulink were used to demonstrate that the proposed controller could compute control actions for each state within a very short timestep, highlighting its computational efficiency. This efficiency underscores the potential for deploying the controller in real-world vehicle autonomous driving systems. Furthermore, we explored the feasibility of a hierarchical control framework comprising deep reinforcement learning (DRL), specifically a Deep Q-Network (DQN)-based high-level controller and a CLF-CBF-QP-based low-level controller. Simulation results show that the vehicle could effectively respond to obstacles and generate a successful trajectory towards its goal. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

As an emerging edge device aimed at consumers, Unmanned Aerial Vehicles (UAVs) have attracted significant attention in the consumer electronics market, particularly for intelligent imaging applications. However, aerial image detection tasks face two major challenges: first, there are numerous small and overlapping objects that are difficult to identify from an aerial perspective, and second, if the detection frame rate is not high enough, missed detections may occur when the UAV is moving quickly, which can negatively impact the user experience by reducing detection accuracy, increasing the likelihood of collision-avoidance failures, and potentially causing unsafe flight behavior. To address these challenges, this paper proposes a novel YOLO (you only look once) framework, named YOLOFLY, which includes a C4f feature extraction module and a DWcDetect head to make the model lightweight, as well as an MPSA attention mechanism and an ACIoU loss function, aimed at improving detection accuracy and performance for consumer-grade UAVs. Extensive experiments on the public VisDrone2019 dataset demonstrate that YOLOFLY outperforms the latest state-of-the-art model, YOLOv11n, by 3.2% in mAP_50-95, reduces detection time by 27.2 ms, decreases the number of parameters by 0.6 M, and cuts floating-point operations by 1.8 B. Finally, testing YOLOFLY in real-world environments also yielded the best results, including a 3.75% reduction in missed detections at high speeds. These findings validate the superiority and effectiveness of YOLOFLY. Full article

Collision avoidance and path planning are essential for ensuring safe and efficient UAV operations, particularly in applications like drone delivery and Advanced Air Mobility (AAM). This study introduces an improved algorithm for three-dimensional path planning in obstacle-rich environments, such as urban and industrial areas. The proposed approach integrates the A* search algorithm with a customized heuristic function which incorporates local obstacle density. This modification not only guides the search towards more efficient paths but also minimizes altitude variations and steers the UAV away from high-density obstacle regions. To achieve this, the A* algorithm was adapted to output obstacle density information at each path node, enabling a subsequent refinement process. The path refinement applies a truncation algorithm that considers both path angles and obstacle density, and the refined waypoints serve as control points for Non-Uniform Rational B-Splines (NURBS) interpolation. This process ensures smooth and dynamically feasible trajectories. Numerical simulations were performed using a quadrotor model with integrated PID controllers in environments with varying obstacle densities. The results demonstrate the algorithm’s ability to effectively balance path efficiency and feasibility. Compared to traditional methods, the proposed approach exhibits superior performance in high-obstacle-density environments, validating its effectiveness and practical applicability. Full article