Safe Path Research Articles

Safe Path Planning Method Based on Collision Prediction for Robotic Roadheader in Narrow Tunnels

Safe path planning is essential for the autonomous operation of robotic roadheader in narrow underground tunnels, where limited perception and the robot’s geometric constraints present significant challenges. Traditional path planning methods often fail to address these issues. This paper proposes a collision prediction-integrated path planning method tailored for robotic roadheader in confined environments. The method comprises two components: collision prediction and path planning. A collision prediction model based on artificial potential fields is developed, considering the non-convex shape of the roadheader and enhancing scalability. By utilizing tunnel design information, a composite potential field model is created for both obstacles and the roadheader, enabling real-time collision forecasting. The A* algorithm is modified to incorporate the robot’s motion constraints, using a segmented weighted heuristic function based on collision predictions. Path smoothness is achieved through Bézier curve smoothing. Experimental results in both obstacle-free and obstacle-laden scenarios show that the proposed method outperforms traditional approaches in terms of computational efficiency, path length, and smoothness, ensuring safe, efficient navigation in narrow tunnels.

Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN

Establishing a safe and stable routing path for a source–destination pair is necessary regardless of the weather conditions. The reason for this is that vehicles can improve safety on the road by exchanging messages and updating each other on the current conditions of both roads and vehicles. This paper intends to solve the problem of when foggy roads make it difficult for drivers to travel, especially when people encounter emergency situations and have no other option but to drive in foggy weather. Although the literature offers few solutions to the problem, no one has considered integrating software-defined networking into vehicular networks for foggy roads to create an optimal routing path. Moreover, it is of significance to mention that vehicles in adverse weather conditions travel following each other and maintaining a constant safety distance, which leads to the formation of a platoon. Considering this, we propose a heterogeneous communication protocol in a software-defined vehicular network to establish an optimal routing path using platoons on foggy highways. Different cases were tested to show how platoons behave in high connectivity and sparsity, achieving a maximum delivery ratio of 99%, a delay of 2 ms, an overhead of 55%, and an acceptable number of hops compared to reference schemes.

YOLOv8-Based XR Smart Glasses Mobility Assistive System for Aiding Outdoor Walking of Visually Impaired Individuals in South Korea

This study proposes an eXtended Reality (XR) glasses-based walking assistance system to support independent and safe outdoor walking for visually impaired people. The system leverages the YOLOv8n deep learning model to recognize walkable areas, public transport facilities, and obstacles in real time and provide appropriate guidance to the user. The core components of the system are Xreal Light Smart Glasses and an Android-based smartphone, which are operated through a mobile application developed using the Unity game engine. The system divides the user’s field of vision into nine zones, assesses the level of danger in each zone, and guides the user along a safe walking path. The YOLOv8n model was trained to recognize sidewalks, pedestrian crossings, bus stops, subway exits, and various obstacles on a smartphone connected to XR glasses and demonstrated an average processing time of 583 ms and an average memory usage of 80 MB, making it suitable for real-time use. The experiments were conducted on a 3.3 km route around Bokjeong Station in South Korea and confirmed that the system works effectively in a variety of walking environments, but recognized the need to improve performance in low-light environments and further testing with visually impaired people. By proposing an innovative walking assistance system that combines XR technology and artificial intelligence, this study is expected to contribute to improving the independent mobility of visually impaired people. Future research will further validate the effectiveness of the system by integrating it with real-time public transport information and conducting extensive experiments with users with varying degrees of visual impairment.

A spherical vector-based adaptive evolutionary particle swarm optimization for UAV path planning under threat conditions

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability.

Global path planning on uneven terrain at construction sites for robots based on an improved A* Algorithm

With the increasing application of construction robots on construction sites, autonomous path planning in unstructured and uneven construction sites has become an urgent challenge. Current path planning approaches face several issues, including prolonged computation times, low efficiency, and redundant nodes, often resulting in impractical paths for robots. Addressing these concerns, this study introduces a global path planning method based on an enhanced A* algorithm to ensure safe and robust navigation of construction robots in such challenging environments. The Improved A* algorithm initially incorporates a bidirectional alternating search strategy to expedite computational speed. Subsequently, it employs path node filtering to mitigate issues associated with bidirectional search and reduce the number of critical nodes, thereby enhancing search efficiency. Furthermore, the introduction of slope constraints decreases the robot's climbing and tilting angles, augmenting the safety of the planned paths. Finally, the paths are smoothed using Bézier curve fitting, facilitating better motion control for the robots. The efficacy of the improved algorithm was validated through experiments on elevation maps with varying terrain and obstacle densities. Simulation results indicate that, compared to the traditional A* algorithm, the Improved A* algorithm reduced computation time by 71.05% to 82.90% and the number of critical nodes by 51.94% to 70.53%, while only increasing the path length by 14.6% to 37.84%. Additionally, there was a significant reduction in climbing and tilting angles, and the paths have become smoother. Therefore, this method not only improves efficiency while generating safe and reliable paths in unstructured and uneven construction sites, but also enables robots to adapt to complex environments, and promotes automation and intelligent construction processes.

Survey of Autonomous Vehicles' Collision Avoidance Algorithms.

Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations. The survey also emphasizes how Machine Learning methods can improve the efficacy of obstacle avoidance. Combined, these techniques are necessary for enhancing the dependability and safety of autonomous driving systems, ultimately increasing public confidence in this game-changing technology.

Image-guided percutaneous mesenteric biopsy: diagnostic yield and safety profile.

To evaluate the diagnostic yield and safety profile of percutaneous image-guided biopsy of mesenteric lesions. Image-guided percutaneous biopsies of the mesentery at a single institution from 2000 to 2022 were identified and reviewed. Relevant demographic and procedural data were abstracted from the medical record. Surgical pathology was reviewed to evaluate if the biopsy was diagnostic and concordant with the patient's final diagnosis. One hundred ninety five patients (mean age, 62.6 ± 14.; M/F, 113/82; mean BMI, 30.4) underwent mesenteric biopsy procedure. Of these, 173 (89%) were performed using ultrasound and 22 (11%) were performed using CT or a hybrid/combined approach. Core needle biopsy was used in 164 (84%) patients, fine-needle aspiration (FNA) was used in 21 (11%), and both were used 10 (5%). Mean/median number of biopsy passes was 2.8 ± 1.3 and 3, respectively (core mean 2.7 ± 1.2; FNA mean 3.4 ± 1.5). Average lesion size was 5.3 ± 4.4cm in the long axis and 2.9 ± 2.0cm in the target plane. Diagnostic yield of core biopsies was 97.7% (n = 170/174) and FNA was 80.6% (n = 25/31) for an overall combined yield of 96.4% (n = 188/195). Of diagnostic biopsies, 95.7% (n = 180/188) were concordant with the final diagnosis, 70.8% (n = 138) of which were considered malignant. Overall concordant diagnostic rate was 180/195 (92.3%). Neuroendocrine tumor pathology was the only factor associated with lower diagnostic yield (13/15, 87%).For all biopsies, average shortest skin-to-target-distance on CT was 6.3 ± 2.8cm, decreased to 4.1 ± 1.3cm with US compression (approximately 35% reduction, p < 0.001). Additionally, ultrasound created a safe path not available on CT in 29 (15%) biopsies. Moderate IV sedation was used in 91.3% (n = 178) of mesenteric procedures. Complications occurred in 11 (5.6%) biopsies, and all were considered minor. This represents a large cohort of image-guided percutaneous biopsies of mesenteric lesions with the majority representing core biopsy performed under US guidance. This technique offers high diagnostic yield and a favorable safety profile for tissue diagnosis. Furthermore, compression with ultrasound reduced skin-to-target distance by 35% and created a biopsy path that would not be possible on CT in 15% of US cases.

STUDI NILAI RESITENSI ELEKTRODA PENTANAHAN MENGGUNAKAN METODE TIGA KUTUB PADA KONDISI TANAH BERBEDA

The grounding system serves as a safe path for electrical current to flow to the ground, which is important in preventing the risk of equipment damage and safety hazards. One of the critical parameters in this system is the resistance of the ground electrode, which can be measured using the three-point method. Based on PUIL 2000, the recommended grounding resistance value for electrical installations must be less than 5 ohms so that the system can work optimally and safely. The three-pole method is known to be accurate because it minimizes the influence of the environment around the electrode. However, factors such as soil type, humidity, and mineral content greatly influence the resistance of the ground electrode. Therefore, this study aims to evaluate the resistance of grounding electrodes in various types of soil, such as clay, swamp soil, damp sandy soil, dry soil, and rocky soil. Research results: The best grounding electrode resistance values ​​were obtained in clay and moist sandy soil (coastal). Meanwhile, the resistance value of the ground electrode for dry soil, rocky soil, and swamp soil does not meet the standard of ≤ 5 ohms. The factors that cause the resistance value of the ground electrode not to meet the standard are because in swampy soil the soil is dominated by insulating organic material and low content of conductive minerals. Also, moisture imbalance, electrode corrosion, and less-than-optimal electrode installation contribute. At the same time, dry soil and rocky soil are characterized by having a high level of resistivity due to low water and mineral content and a texture that does not support optimal electrical conductivity where field measurement results are not yet available. Meets the standard ≤ 5 Ω

A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning.

Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety. This paper presents a novel approach using Lyapunov Optimization (LO) for local path planning in AVs. The proposed LO model is benchmarked against two conventional methods: model predictive control and a sampling-based approach. Additionally, an AV prototype was developed and tested in Norman, Oklahoma, where it collected data to evaluate the performance of the three control algorithms used in this study. To minimize costs and increase real-world applicability, a vision-only solution was employed for object detection and the generation of bird's-eye-view coordinate data. Each control algorithm, i.e., Lyapunov Optimization (LO) and the two baseline methods, were independently used to generate safe and smooth paths for the AV based on the collected data. The approaches were then compared in terms of path smoothness, safety, and computation time. Notably, the proposed LO strategy demonstrated at least a 20 times reduction in computation time compared to the baseline methods.

Energy-Constrained Safe Path Planning for UAV-Assisted Data Collection of Mobile IoT Devices

Energy-Constrained Safe Path Planning for UAV-Assisted Data Collection of Mobile IoT Devices

Mixed Multi-Strategy Improved Aquila Optimizer and Its Application in Path Planning

With the growing prevalence of drone technology across various sectors, efficient and safe path planning has emerged as a critical research priority. Traditional Aquila Optimizers, while effective, face limitations such as uneven population initialization, a tendency to get trapped in local optima, and slow convergence rates. This study presents a multi-strategy fusion of the improved Aquila Optimizer, aiming to enhance its performance by integrating diverse optimization techniques, particularly in the context of path planning. Key enhancements include the integration of Bernoulli chaotic mapping to improve initial population diversity, a spiral stepping strategy to boost search precision and diversity, and a “stealing” mechanism from the Dung Beetle Optimization algorithm to enhance global search capabilities and convergence. Additionally, a nonlinear balance factor is employed to dynamically manage the exploration–exploitation trade-off, thereby increasing the optimization of speed and accuracy. The effectiveness of the mixed multi-strategy improved Aquila Optimizer is validated through simulations on benchmark test functions, CEC2017 complex functions, and path planning scenarios. Comparative analysis with seven other optimization algorithms reveals that the proposed method significantly improves both convergence speed and optimization accuracy. These findings highlight the potential of mixed multi-strategy improved Aquila Optimizer in advancing drone path planning performance, offering enhanced safety and efficiency.

Expedited and safe discharge path for extraction patients utilizing a temporary permanent pacemaker

Expedited and safe discharge path for extraction patients utilizing a temporary permanent pacemaker

Numerical analysis on the ducted propeller aerodynamics in sidewall-ground effect

Owing to their compact structure and robust protective features, unmanned aerial vehicles (UAVs) equipped with ducted propellers are particularly suited for search and detection missions in confined environments. However, in such spaces, proximity effects can lead to pronounced instability in the aerodynamic performance of the UAV, particularly under the influence of multiple wall interactions. This study employs a sliding mesh technique and the Unsteady Reynolds-Averaged Navier-Stokes method to perform computational fluid dynamics simulations, analyzing the ground-sidewall effect's impact on ducted propeller aerodynamic performance across various hovering positions. Research shows that sidewall effects remain largely unaffected by ground effects. However, when the ground height is less than 2r and the sidewall distance is less than r, the ground effect noticeably alters the strength of the sidewall effect. In this region, sidewall suction effects increase sharply as ground height decreases; however, once the ground height falls below 1r, the mean side force diminishes rapidly. Based on the simulation results, this study proposes an empirical formula for side force under coupled sidewall-ground effects, with a mean absolute percentage error of approximately 10% compared to simulation results. Through an analysis of the unstable motion of vortex structures, this study further explains the causes of substantial transient force fluctuations observed near the walls. The findings of this study provide theoretical guidance for the design of flight controllers and the planning of safe flight paths in confined environments.

Lane change path planning using sweeping region for full-sized autonomous driving bus in urban environment

This paper presents a path planning method for the lane change maneuver of a full-sized autonomous driving bus in urban environment. A sampling method is incorporated for lane change path planning. Path candidates for lane changes are generated using the pattern of a quintic polynomial function. For optimal path selection among the candidates, a cost function and constraints are designed and applied, considering ride quality, lane change efficiency, and safety. Safety conditions account for lane departure and collision with obstacles. Considering large vehicles such as buses, the region swept by the vehicle body is derived by exploiting the geometrical relationship of the control point and corners, assuming that the control point follows the investigated path candidate. The sweeping region is utilized for accurate and efficient evaluation of the safety condition of the path candidate. Subsequently, longitudinal motion is planned based on Model Predictive Control (MPC) to maintain adequate clearance with surrounding vehicles and follow the desired speed. The proposed algorithm has been evaluated based on closed-loop simulations and vehicle-in-the-loop tests. The test results show that the proposed algorithm plans a safe lane change path, ensuring the vehicle body remains within the safe region. Additionally, the proposed algorithm is shown to enhance real-time performance.

Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

Abstract This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study.

Drivable path detection for a mobile robot with differential drive using a deep Learning based segmentation method for indoor navigation.

The integration of artificial intelligence into the field of robotics enables robots to perform their tasks more meaningfully. In particular, deep-learning methods contribute significantly to robots becoming intelligent cybernetic systems. The effective use of deep-learning mobile cyber-physical systems has enabled mobile robots to become more intelligent. This effective use of deep learning can also help mobile robots determine a safe path. The drivable pathfinding problem involves a mobile robot finding the path to a target in a challenging environment with obstacles. In this paper, a semantic-segmentation-based drivable path detection method is presented for use in the indoor navigation of mobile robots. The proposed method uses a perspective transformation strategy based on transforming high-accuracy segmented images into real-world space. This transformation enables the motion space to be divided into grids, based on the image perceived in a real-world space. A grid-based RRT* navigation strategy was developed that uses images divided into grids to enable the mobile robot to avoid obstacles and meet the optimal path requirements. Smoothing was performed to improve the path planning of the grid-based RRT* and avoid unnecessary turning angles of the mobile robot. Thus, the mobile robot could reach the target in an optimum manner in the drivable area determined by segmentation. Deeplabv3+ and ResNet50 backbone architecture with superior segmentation ability are proposed for accurate determination of drivable path. Gaussian filter was used to reduce the noise caused by segmentation. In addition, multi-otsu thresholding was used to improve the masked images in multiple classes. The segmentation model and backbone architecture were compared in terms of their performance using different methods. DeepLabv3+ and ResNet50 backbone architectures outperformed the other compared methods by 0.21%-4.18% on many metrics. In addition, a mobile robot design is presented to test the proposed drivable path determination method. This design validates the proposed method by using different scenarios in an indoor environment.

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active-Matrix Digital Microfluidics (AM-DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi-target, collision-free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL-60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM-DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology.

Safe and Efficient Path Planning Under Uncertainty via Deep Collision Probability Fields

Safe and Efficient Path Planning Under Uncertainty via Deep Collision Probability Fields

Research on trajectory planning based on optimal control modeling

With the advancement of autonomous driving technology, trajectory planning has emerged as one of the core technologies to achieve safe and efficient vehicle driving. An algorithm for single-vehicle trajectory planning based on the Optimal Control Problem (OCP) is proposed in this paper, which can not only plan a safe and efficient path for autonomous vehicles, but also realize the obstacle avoidance function. Firstly, the OCP is modeled for the cycle trajectory planning problem. Secondly, this paper transforms it into a Nonlinear Programming (NLP) with all discrete methods, and uses the interior point method to solve it. Then, the Y-type intersection is selected as the verification scene, and through a series of simulation experiments, the efficiency and safety of the algorithm in different driving environments are verified. The model and algorithm proposed in this paper are not only applicable to Y-type intersections, but also can be extended to other complex traffic roads, providing a new idea and method for trajectory planning in intelligent transportation systems.

Low-cost guide dog robot navigation using Dueling DQN

Abstract. Traditional aids for the visually impaired, such as guide dogs, have limitations. They are expensive to train, require a long preparation time, and are not accessible to most people. Guide robots have the potential to address these issues. However, most existing navigation methods focus solely on the robot's trajectory, neglecting the movement of the person. As a result, the paths of the visually impaired individual and the robot may not overlap or may intersect with obstacles.To overcome these challenges, we propose an intelligent guide dog system based on a Deep Q-network (DQN). First, a kinematic model is constructed to calculate the relative positions of the guide robot and the human. Then, the reward function is designed based on this kinematic model. Finally, the Dueling DQN is trained to plan a path and predict the human's possible trajectory, ensuring collision avoidance for both the robot and the human.Our experiments demonstrated that our system performs well in both simulated environments and in real-world scenarios, such as climbing stairs. The robot can accurately find a safe path that considers the trajectories of both the robot and the human, successfully completing tasks safely.