Navigation Accuracy Research Articles

A Method for Recognition and Coordinate Reference of Autonomous Underwater Vehicles to Inspected Objects of Industrial Subsea Structures Using Stereo Images

To date, the development of unmanned technologies using autonomous underwater vehicles (AUVs) has become an urgent demand for solving the problem of inspecting industrial subsea structures. A key issue here is the precise localization of AUVs relative to underwater objects. However, the impossibility of using GPS and the presence of various interferences associated with the dynamics of the underwater environment do not allow high-precision navigation based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative technology involves the processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to the technology of acoustic measurement processing. Although there have been results in this direction, further development of methods for extracting spatial information about objects from images recorded by a camera is necessary in the task of calculating the exact mutual position of the AUV and the object. In this study, in the context of the problem of subsea production system inspection, we propose a technology to recognize underwater objects and provide coordinate references to the AUV based on stereo-image processing. Its distinctive features are the use of a non-standard technique to generate a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object. The results of experiments on virtual scenes and with real data showed the effectiveness of the proposed technology.

Intraoral Scanning Enables Virtual-Splint-Based Non-Invasive Registration Protocol for Maxillofacial Surgical Navigation.

Background/Objectives: Surgical navigation has advanced maxillofacial surgery since the 1990s, bringing benefits for various indications. Traditional registration methods use fiducial markers that are either invasively bone-anchored or attached to a dental vacuum splint and offer high accuracy but necessitate additional imaging with increased radiation exposure. We propose a novel, non-invasive registration protocol using a CAD/CAM dental splint based on high-resolution intraoral scans. Methods: The effectiveness of this method was experimentally evaluated with an ex vivo 3D-printed skull measuring the target registration error (TRE). Surgical application is demonstrated in two clinical cases. Results: In the ex vivo model, the new CAD/CAM-splint-based method achieved a mean TRE across the whole facial skull of 0.97 ± 0.29 mm, which was comparable to traditional techniques like using bone-anchored screws (1.02 ± 0.23 mm) and dental vacuum splints (1.01 ± 0.33 mm), while dental anatomical landmarks showed a lower accuracy with a mean TRE of 1.84 ± 0.44 mm. Multifactorial ANOVA confirmed significant differences in TRE based on the registration method and the navigated level of the facial skull (p < 0.001). In clinical applications, the presented method demonstrated high accuracy for both midfacial and mandibular surgeries. Conclusions: Our results suggest that this non-invasive CAD/CAM-splint-based method is a viable alternative to traditional fiducial marker techniques, with the potential for broad application in maxillofacial surgery. This approach retains high accuracy while eliminating the need for supplementary imaging and reduces patient radiation exposure. Further clinical trials are necessary to confirm these findings and optimize splint design for enhanced navigational accuracy.

Embedded ML-based locomotion control for a 12-joint four-legged robot

Reinforcement learning has developed as a promising approach for robot locomotion control, which can save engineering effort compared to conventional approaches. This article presents the implementation of Reinforcement learning on a low-cost, 12 degree of freedom robot known as a quadruped (spider) to optimize locomotion control, enabling the robot to move on different surfaces such as flat surfaces, ramps, speed bumps, and rough terrain. A MATLAB Simulink model is developed as a digital twin of the spider robot. The dynamics of the model were studied and validated using an open-loop algorithm. Then, the model is utilized in a training simulation environment to apply the reinforcement learning algorithm, showing its ability to move along a predefined path as a replacement for conventional motion control systems. Moreover, the work compares the performance and effectiveness of machine learning-based locomotion control with traditional motion control systems regarding navigation accuracy, speed, and adaptability in challenging environments. The minimum hardware requirements are also studied to move the experiment from simulation to reality.

Accuracy, rationality and specialization in a generalized model of collective navigation.

Animal navigation is a key behavioural process, from localized foraging to global migration. Within groups, individuals may improve their navigational accuracy by following those with more experience or knowledge, by pooling information from many directional estimates ('many wrongs') or some combination of these strategies. Previous agent-based simulations have highlighted that homogeneous leaderless groups can improve their collective navigation accuracy when individuals preferentially copy the movement directions of their neighbours while giving a low weighting to their own navigational knowledge. Meanwhile, other studies have demonstrated how specialized leaders may emerge, and that a small number of such individuals can improve group-level navigation performance. However, in general, these earlier results either lack a full mathematical grounding or do not fully consider the effect of individual self-interest. Here we derive and analyse a mathematically tractable model of collective navigation. We demonstrate that collective navigation is compromised when individuals seek to optimize their own accuracy in both homogeneous groups and those with differing navigational abilities. We further demonstrate how heterogeneous navigational strategies (specialized leaders and followers) may evolve within the model. Our results thus unify different lines of research in collective navigation and highlight the importance of individual selection in determining group composition and performance.

Star angle modified with relativistic effects/StarNAV integrated navigation method for Mars exploration

The celestial navigation system based on star angle (SA) is a classical autonomous navigation method for the spacecraft, which directly provides the position information of the spacecraft relative to the near celestial body. But due to the relativistic effects, the star direction observed by spacecraft is inconsistent with that acquired from star ephemeris, which reduces navigation accuracy of SA. In addition, SA cannot directly provide the velocity information of the spacecraft. StarNAV is a novel celestial navigation method that utilizes the relativistic effects, which mostly provides the velocity information of the spacecraft. In this paper, the star angle modified with relativistic effects (SAMRE)/StarNAV integrated navigation method is proposed. The measurement model of SAMRE is established by considering relativistic effects in the measurement model of SA. Simulation results indicate that during the Mars approach phase, SAMRE has better navigation accuracy compared with SA, and the navigation accuracy of the SAMRE/StarNAV integrated navigation method is higher than that of SAMRE, StarNAV and SA/StarNAV, respectively. Furthermore, the paper analyses the impact of measurement errors on the navigation accuracy of SAMRE/StarNAV.

Evolutionary Optimization for Unmanned Underwater Vehicle Navigation

Unmanned Underwater Vehicles (UUVs) play a vital role in various underwater exploration and surveillance tasks. However, the effective navigation of UUVs in complex underwater environments poses significant challenges due to factors such as limited communication, dynamic currents, and other difficulties. Evolutionary optimization techniques have developed as promising tools for enhancing UUV navigation abilities. This review provides a brief overview of the application of evolutionary optimization algorithms, including genetic algorithms, evolutionary approaches, and particle swarm optimization, in the context of UUV navigation. The fundamental principles of these algorithms and their applications in path planning, path optimization, localization, obstacle avoidance, and mission planning for UUVs are discussed in brief. Through an analysis of existing literature and case studies, the use of evolutionary optimization in improving the navigation efficiency, accuracy, and robustness of UUVs is highlighted. Additionally, current challenges were identified, and future research directions to advance the integration of evolutionary optimization techniques in UUV navigation systems are also discussed. Overall, this aims to provide insights into the potential of evolutionary optimization for addressing the navigation challenges faced by unmanned underwater vehicles and promoting advancements in underwater exploration and surveillance technologies.

A Detailed Analysis of the Dynamic Behavior of a MEMS Vibrating Internal Ring Gyroscope.

This paper presents the development of an analytical model of an internal vibrating ring gyroscope in a Microelectromechanical System (MEMS). The internal ring structure consists of eight semicircular beams that are attached to the externally placed anchors. This research work analyzes the vibrating ring gyroscope's in-plane displacement behavior and the resulting elliptical vibrational modes. The elliptical vibrational modes appear as pairs with the same resonance frequency due to the symmetric structure of the design. The analysis commences by conceptualizing the ring as a geometric structure with a circular shape possessing specific dimensions such as thickness, height, and radius. We construct a linear model that characterizes the vibrational dynamics of the internal vibrating ring. The analysis develops a comprehensive mathematical formulation for the radial and tangential displacements in local polar coordinates by considering the inextensional displacement of the ring structure. By utilizing the derived motion equations, we highlight the underlying relationships driving the vibrational characteristics of the MEMS' vibrating ring gyroscope. These dynamic vibrational relationships are essential in enabling the vibrating ring gyroscope's future utilization in accurate navigation and motion sensing technologies.

Visual Navigation of Caged Chicken Coop Inspection Robot Based on Road Features.

The speed and accuracy of navigation road extraction and driving stability affect the inspection accuracy of cage chicken coop inspection robots. In this paper, a new grayscale factor (4B-3R-2G) was proposed to achieve fast and accurate road extraction, and a navigation line fitting algorithm based on the road boundary features was proposed to improve the stability of the algorithm. The proposed grayscale factor achieved 92.918% segmentation accuracy, and the speed was six times faster than the deep learning model. The experimental results showed that at the speed of 0.348 m/s, the maximum deviation of the visual navigation was 4 cm, the average deviation was 1.561 cm, the maximum acceleration was 1.122 m/s2, and the average acceleration was 0.292 m/s2, with the detection number and accuracy increased by 21.125% and 1.228%, respectively. Compared with inertial navigation, visual navigation can significantly improve the navigation accuracy and stability of the inspection robot and lead to better inspection effects. The visual navigation system proposed in this paper has better driving stability, higher inspection efficiency, better inspection effect, and lower operating costs, which is of great significance to promote the automation process of large-scale cage chicken breeding and realize rapid and accurate monitoring.

An Experimental Study of Odometer as a Navigation aid for Land Vehicle Application

Strap-down inertial navigation system (SINS) shall provide position, velocity, and orientation information with reference to a pre-defined reference frame. The SINS shall have excellent accuracy over a short duration and is highly self-contained. However, the errors in navigation solutions build up exponentially with time and make the system output very unstable. In order to mitigate these errors, there is a need for a damping mechanism through external aiding sensors. In this article, one such approach is proposed to improve navigation accuracy for land vehicles in the GNSS (Global Navigation Satellite Systems) denied environment. The design and implementation of an odometer are carried out with the use of inductive proximity sensors and a mounting assembly to attach the odometer to one of the non-steering wheels of a vehicle. The odometer gives the pulse-high and pulse-low output with reference to the rotation of the wheel to which it is attached. Vehicle ground velocity is derived through sensed output pulses processed through quadrature decoder circuitry. Odometer measurements along with non-holonomic constraints (NHC) are used for minimizing velocity and position errors in SINS using an extended Kalman Filter (EKF) technique. Field trials are carried out to validate the proposed scheme of hybrid navigation with odometer design and experimental results are presented with a positioning accuracy of 0.05 % of distance travelled (DT).

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

A Low-Cost GNSS/INS integration method aided by Cascade-LSTM Pseudo-Velocity measurement for bridging GNSS outages

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) together overcome their individual shortcomings, providing accurate navigation results in GNSS/INS integrated systems. However, the entire system’s positioning accuracy deteriorates over time during GNSS outages. In this paper, a pseudo-velocity measurement predicted by a Cascade-LSTM is introduced to GNSS/INS integration to bridge GNSS outages. Specifically, the Cascade-LSTM neural network, which is sensitive to angular features and suitable for processing multi-scale time series data, is proposed to predict GNSS position increments. A pseudo-velocity measurement update model for Kalman Filter (KF) is then established to incorporate these predictions into GNSS/INS integration during GNSS outages. GNSS-prohibited situation was simulated on road field data collected using a low-cost GNSS/INS integrated system to evaluate the proposed method. The comparison results demonstrate that the proposed method outperforms KF-only and the conventional pseudo-position measurement aided KF, especially in situations involving long GNSS outages and significant vehicle dynamics.

Real-time vision-inertial landing navigation for fixed-wing aircraft with CFC-CKF

Vision-inertial navigation offers a promising solution for aircraft to estimate ego-motion accurately in environments devoid of Global Navigation Satellite System (GNSS). However, existing approaches have limited adaptability for fixed-wing aircraft with high maneuverability and insufficient visual features, problems of low accuracy and subpar real-time arise. This paper introduces a novel vision-inertial heterogeneous data fusion methodology, aiming to enhance the navigation accuracy and computational efficiency of fixed-wing aircraft landing navigation. The visual front-end of the system extracts multi-scale infrared runway features and computes geo-reference runway image as observation. The infrared runway features are recognized efficiently and robustly by a lightweight end-to-end neural network from blurry infrared images, and the geo-reference runway is generated through projection of the runway’s prior geographical information and prior pose. The fusion back-end of the navigation system is the Covariance Feedback Control based Cubature Kalman Filter (CFC-CKF) framework, which tightly integrates visual observations and inertial measurements for zero-drift pose estimation and curbs the effect of inaccurate kinematic noise statistics. Finally, real flight experiments demonstrate that the algorithm can estimate the pose at a frequency of 100 Hz and fulfill the navigation accuracy requirements for high-speed landing of fixed-wing aircraft.

Long-range LBL underwater acoustic navigation considering Earth curvature and Doppler effect

The accuracy of underwater acoustic navigation is significantly influenced by geometry configuration and systematic errors coming from geophysical environment. Long baseline (LBL) systems exhibit superior positioning precision than other acoustic navigation systems, primarily attributable to the better Geometric Dilution of Precision (GDOP). However, the geometry configuration tends to degrade when the underwater vehicle is far away from the seafloor beacons. Factors such as acoustic ray bending error, Doppler effect and Earth curvature may seriously affect the navigation accuracy. To address these issues, we present a robust Kalman filter with systematic error compensation for long-range LBL systems. Firstly, a reversed acoustic ray tracking method is proposed for a unique phenomenon in acoustic wave propagation. Following that, we construct an acoustic positioning model with a time bias parameter, to weaken the impact of Doppler effect on acoustic ranging. This additional parameter was estimated with the state of the user vehicle in real time. At last, an observation equation of pressure gauge considering Earth curvature is presented for depth constraint. Long-range LBL experiments conducted in the South China Sea is employed to validate the performance of the proposed methods, taking the positioning results of inverted ultra-short baseline (iUSBL) system as reference. The results show that the proposed method outperform traditional method with a decrease of about 60 % in the three-dimensional root mean square (3DRMS) of navigation errors. With mentioned three improvements, the 3DRMS of the long-range LBL system with the longest distance of 20 km from the beacon center is less than 14 m. These findings can provide theoretical basis for other long-range LBL systems.

A robust integrated navigation optimization method for USV in signal occlusion environment

Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

The Impact of Navigation in Lumbar Spine Surgery: A Study of Historical Aspects, Current Techniques and Future Directions.

Background/Objectives: We sought to improve accuracy while minimizing radiation hazards, improving surgical outcomes, and preventing potential complications. Despite the increasing popularity of these systems, a limited number of papers have been published addressing the historical evolution, detailing the areas of use, and discussing the advantages and disadvantages, of this increasingly popular system in lumbar spine surgery. Our objective was to offer readers a concise overview of navigation system history in lumbar spine surgeries, the techniques involved, the advantages and disadvantages, and suggestions for future enhancements to the system. Methods: A comprehensive review of the literature was conducted, focusing on the development and implementation of navigation systems in lumbar spine surgeries. Our sources include PubMed-indexed peer-reviewed journals, clinical trial data, and case studies involving technologies such as computer-assisted surgery (CAS), image-guided surgery (IGS), and robotic-assisted systems. Results: To develop more practical, effective, and accurate navigation techniques for spine surgery, consistent advancements have been made over the past four decades. This technological progress began in the late 20th century and has since encompassed image-guided surgery, intraoperative imaging, advanced navigation combined with robotic assistance, and artificial intelligence. These technological advancements have significantly improved the accuracy of implant placement, reducing the risk of misplacement and related complications. Navigation has also been found to be particularly useful in tumor resection and minimally invasive surgery (MIS), where conventional anatomic landmarks are lacking or, in the case of MIS, not visible. Additionally, these innovations have led to shorter operative times, decreased radiation exposure for patients and surgical teams, and lower rates of reoperation. As navigation technology continues to evolve, future innovations are anticipated to further enhance the capabilities and accessibility of these systems, ultimately leading to improved patient outcomes in lumbar spine surgery. Conclusions: The initial limited utilization of navigation system in spine surgery has further expanded to encompass almost all fields of lumbar spine surgeries. As the cost-effectiveness and number of trained surgeons improve, a wider use of the system will be ensured so that the navigation system will be an indispensable tool in lumbar spine surgery. However, continued research and development, along with training programs for surgeons, are essential to fully realize the potential of these technologies in clinical practice.

Space optical communication system for space optical networks and deep space exploration

The acquisition time of tens to hundreds of seconds in the optical link between satellites makes it difficult to meet the needs of constructing spatial optical networks. In addition, as a basic requirement for deep space explorers, autonomous attitude determination and autonomous navigation demand the installation of separate, expensive, and complex inertial devices, and the communication data rate is too low to meet the timely transmission of large amounts of data. In this paper, we proposed and developed a multifunctional fusion space optical communication system for space optical networks and deep space exploration, which has the functions of autonomous attitude determination, autonomous navigation, and high-speed optical communication. The sub-second acquisition time can meet the requirements of space optical network construction, and the ability of autonomous attitude determination and autonomous navigation significantly reduce the amount of R&amp;D expenses of the explorer; decrease the volume, weight, and power consumption of the explorer; and improve the reliability and autonomous survival ability of the explorer. The paper provides the structure, working principle, and main algorithm models and conducts a feasibility analysis and field experiments. The experimental results showed that the average open-loop pointing accuracy of the optical terminal is 95.8 µrad (attitude determination accuracy), which can be improved to 39.1 µrad after filtering, and the acquisition time is less than 1 s. For deep space exploration, the navigation accuracy is less than 67.6 km in the cruise phase and 10 km in the acquisition phase, and field experiments have also proven its feasibility. The significance of our research work lies in proposing what we believe to be a new system operation scheme and design method for optical communication systems, and its results can be widely applied in all fields of space optical communication.

Infinity: A Prospective Trial for Safety and Accuracy of Navigated Posterior Cervical and Thoracic Instrumentation in long-segment Fusions.

Unblinded single-arm prospective clinical trial. Evaluate safety and accuracy of navigation for placement of posterior cervicothoracic instrumentation. Computer assisted stereotactic navigation for placement of spinal instrumentation has been widely studied and implemented in the thoracic and lumbar spine. However less literature exists regarding the use of computer assisted navigation for posterior cervical instrumentation, particularly with lateral mass fixation. Here we present the first prospective study of navigated cervical lateral mass screw placement for cervicothoracic fusion. Patients who met indications for posterior cervical fusion were screened, consented, and enrolled preoperatively for instrumentation with Medtronic Infinity Occipital-Cervical-Thoracic implants, with use of intraoperative O-arm and stereotactic Stealth navigation. Postoperative CTs of the instrumented levels were obtained during the same hospital admission. Primary outcome of the trial was safety. Secondary outcomes were screw accuracy assessed by Gertzbein-Robbins grade, neurologic exams, and patient reported outcomes on the PROMIS 29 questionnaire. A total of 50 patients underwent surgery, and 557 screws were placed. There were no adverse events related to the use of navigation or screw malposition. Gertzbein-Robbins grade A or B placement comprised 95% of navigated screws. There was a decrease in positive Hoffmann sign rate postoperatively, and sensory and motor exams remained stable. There was improvement in patient reported pain and sleep domains. Navigation for cervicothoracic instrumentation is safe overall and leads to high rates of accurately placed screws. Longer term follow up could provide more insight to whether the use of this technology results in durable improvement in spinal alignment parameters and patient reported outcomes. 3.

Bio-inspired machine-learning aided geo-magnetic field based AUV navigation system

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.

Leaderless consensus decision-making determines cooperative transport direction in weaver ants.

Animal groups need to achieve and maintain consensus to minimize conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behaviour, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here, we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a 'wisdom-of-the-crowd' strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.

Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies, particularly in drone swarms. The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments. In this context, this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks. The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles, such as buildings. The algorithm incorporates three key components to optimize the obstacle detection, navigation, and energy efficiency within a drone swarm. Firstly, the algorithm utilizes a method for calculating the position of a virtual leader, acting as a navigational beacon to influence the overall direction of the swarm. Secondly, the algorithm identifies observers within the swarm based on the current orientation. To further refine obstacle avoidance, the third component involves the calculation of angular velocity using fuzzy logic. This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location, allowing for a nuanced and adaptable computation of angular velocity. The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically, ensuring practical obstacle avoidance. The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations. The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.