Obstacle Detection Research Articles

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Sound Metric Proximity Hearing App

Globally, hearing impairment affects over 466 million people, creating barriers to accessing audiometric testing and timely diagnosis. Conventional methods often prove challenging to access, especially for people with limited resources or any other disadvantages. The proposed innovative "Sound Metric Proximity Hearing App" aims to revolutionize auditory health through mobile technology. Utilizing advanced techniques such as frequency-specific sound generation, individual ear sound production, and obstacle detection, the app provides comprehensive hearing tests through a user-friendly platform. The application is developed using React Native for the frontend and Django for the backend. It integrates seamlessly with mobile devices and databases, ensuring efficient and reliable performance. With simple result export and tools for early detection, the application enhances the accessibility and efficiency in hearing assessments. It bridges gaps in traditional testing methods, empowering users to proactively manage their auditory health. The "Sound Metric Proximity Hearing App" addresses the essential need for accessible, efficient hearing tests, making a positive impact on global auditory health. Through its innovative approach, the app aims to reduce the stigma associated with hearing loss and promote early intervention. By providing a convenient tool for educators, the app supports classroom management and improves learning environments. With its potential to reach unprivileged people and enhance awareness, the proposed sound metric application is devised to contribute significantly to global auditory health.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Obstacle detection to minimize delay and Q-learning to improve routing efficiency in VANET

Nowadays, several service providers in urban areas significantly consider vehicular ad hoc networks (VANET). VANETs can enhance road safety, prevent accidents, and grant passengers entertainment. Though in VANET, efficient routing has remained an open problem. VANET is dynamic; the frequent update in the situation originates through several aspects, such as traffic conditions and updates in the road topology, which demand a suitably adaptive routing. The existence of blocking obstacles degrades routing approaches and increases the failure of paths. These issues build an excessive amount of resource utilization and increase network delay. To solve these issues, obstacle detection to minimize delay and Q-learning to improve routing efficiency (ODQI) in VANET is proposed. This mechanism uses the spanning tree algorithm detects the obstacle. Clustering can be used to manage the topology in VANETs. The dingo algorithm selects the best cluster head (CH) based on vehicle bandwidth, speed, and link lifespan. Furthermore, the sender forwards the traffic information from the sender to the receiver by applying a Q-learning algorithm. This learning algorithm computes the award function to choose the forwarder, improving the routing efficiency. Simulation results demonstrate that the ODQI mechanism increases the CH lifetime and minimizes the network delay.

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Integrating deep reinforcement learning and improved artificial potential field method for safe path planning for mobile robots

Purpose This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods. Design/methodology/approach The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point. Findings Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots. Originality/value This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

Collaborative Obstacle Detection for Dual USVs Using MGNN-DANet with Movable Virtual Nodes and Double Attention

To reduce missed detections in LiDAR-based obstacle detection, this paper proposes a dual unmanned surface vessels (USVs) obstacle detection method using the MGNN-DANet template matching framework. Firstly, point cloud templates for each USV are created, and a clustering algorithm extracts suspected targets from the point clouds captured by a single USV. Secondly, a graph neural network model based on the movable virtual nodes is designed, introducing a neighborhood distribution uniformity metric. This model enhances the local point cloud distribution features of the templates and suspected targets through a local sampling strategy. Furthermore, a feature matching model based on double attention is developed, employing self-attention to aggregate the features of the templates and cross-attention to evaluate the similarity between suspected targets and aggregated templates, thereby identifying and locating another USV within the targets detected by each USV. Finally, the deviation between the measured and true positions of one USV is used to correct the point clouds obtained by the other USV, and obstacle positions are annotated through dual-view point cloud clustering. Experimental results show that, compared to single USV detection methods, the proposed method reduces the missed detection rate of maritime obstacles by 7.88% to 14.69%.

A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios.

Calibration and validation of a multi-beam LiDAR onboard China Chang’e lunar probe for landing obstacle avoidance

For automatic exploration of specific areas with high scientific value on the moon and other planets, high-precision obstacle detection and hazard avoidance capabilities are required. Three-dimensional (3D) LiDAR can obtain a high-resolution 3D point cloud, offering significant advantages in landing obstacle avoidance. During the landing of the Chang’e lander, a laser 3D imaging sensor (L3DIS) is used to measure and generate accurate topographic maps of the candidate landing area in real-time to help find a safe landing zone. The L3DIS employs 16 beamlets split from a Gaussian laser beam and 16 channels of linear-array avalanche photodiodes within the same optical path and scans the object with a two-axis galvanometer in a field of view of 29°×33°. The calibration of the systematic errors and the performance of obstacle detection on this sensor are conducted in this paper. First, the instrument components and main error sources of the sensor are introduced, and the systematic errors are calibrated based on planar targets, indicating that the sensor’s accuracy is greater than 4 cm, and the corresponding obstacle detection accuracy is better than 12 cm. Second, the ground validation of obstacle detection is demonstrated, and the obtained point cloud is compared with that obtained by a terrestrial laser scanner, indicating that the sensor has good performance. Finally, the performance of onboard measurement and obstacle detection is analyzed, showing that the sensor can identify obvious craters and rocks, i.e., nine major craters with a maximum and minimum depth of 1.36 m and 0.16 m, respectively, and major rocks with a maximum and minimum height of 0.1 m and 0.05 m, respectively, and main landing area with slop less than 10° except for the edges of craters and rocks.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Vision-Based obstacle detection in dangerous region of coal mine driverless rail electric locomotives

In order to solve the problem of inaccurate obstacle detection as well as frequent start-stops caused by oversensitive obstacle detection in existing driverless rail electric locomotives in underground coal mines, the YOLO-Region model is proposed to realize regional obstacle detection. First, the model backbone uses InceptionNeXt block and the developed New Spatial Pyramid Pooling (NSPP) module; the model neck extends the FPN+PAN architecture; the model head uses improved task-specific context decoupling (Impro-TSCODE) head. In addition, repulsion loss is introduced to improve the detection ability of partially occluded targets. The experimental results show that the YOLO-Region achieves competitive detection performance with mAP of 98.0 % and an average detection speed of 94.5 FPS. Second, a vision-based method for defining dangerous region based on pixel coordinate points is developed and integrated into YOLO-Region, which allows the model to detect obstacles only within a specific region, thereby reducing frequent start-stops of driverless electric locomotives.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

An Assistant System For Blind To Avoid Obstacles Using Artificial Intelligence Techniques

This study focuses on developing an assistive system for blind individuals for collision avoidance of obstacles by combining artificial intelligence techniques; Convolutional Neural Networks (CNN), fuzzy logic control (FLC), and genetic algorithms(GA), This integrated system, named the (NFG) Neural Fuzzy Genetic). The proposed system combines artificial intelligence techniques through detecting and tracking objects, measuring the distance between objects and the blind person, and providing movement guidance using three ultrasonic sensors with FLC and optimization GA. The integration of these technologies offers an innovative solution to enhance the mobility and safety of blind individuals.Specifically, object detection and tracking are applied through CNN, with an obstacle detection range of up to 40 meters. The obstacle recognition system is trained on the ResNet50 model, which includes 50 million trained images and more than 1,000 obstacle classifications, resulting in high accuracy in identifying and detecting obstacles. When tested, the accuracy of the trainer model reached 99.9%. FLC is then used to provide motor guidance and help make appropriate decisions in the presence of obstacles, navigate safely and independently, and determine movement directions in obstacle-free paths with the help of three sensors.

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.

CVR-LSE: Compact Vectorized Representation of Local Static Environments for Reliable Obstacle Detection

CVR-LSE: Compact Vectorized Representation of Local Static Environments for Reliable Obstacle Detection

LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings.

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

A real-time and high-accuracy railway obstacle detection method using lightweight CNN and improved transformer

With the sustained development of railway transportation, the urgent necessity to improve train operation safety makes obstacle detection become the research focus. However, existing railway obstacle detectors still face challenges in balancing detection accuracy and speed during the shunting process. In addition, they are not robust enough in real-world railway environments, especially in complex scenes involving small obstacles. To address these problems, this paper presents a real-time and high-accuracy railway obstacle detection model using lightweight CNN and improved transformer (RH-Net) for detecting railway obstacles efficiently to guarantee traffic safety. First, the Lightweight Feature Extraction Module (LEM) is designed to minimize the model’s computational load while maintaining its feature extraction ability. Then, the Improved Transformer Module (IFM) is developed to boost the model’s ability about stably extract global contextual information. Finally, the Enhanced Multi-Scale Feature Fusion Module (EFM) is proposed to optimize the detection of obstacles with different sizes, especially small objects. In the experiments on railway dataset, RH-Net achieves optimal detection performance on GeForce GTX 1080Ti (96.99% mAP and 135 FPS) and Jetson Xavier NX (97.02% mAP and 43 FPS), which is significantly superior to the existing detection models. Experimental results show that RH-Net has excellent detection ability, which can accurately and efficiently detect obstacles in complicated railway environments. Moreover, the experiments on MS COCO indicate that RH-Net can achieve more satisfactory detection performance than existing state-of-the-art methods. Therefore, the proposed model can be well-applied to more complex real-world scenes for multiple object detection.