Search Results (41)

This paper presents a visual active SLAM method considering measurement and state uncertainty for space exploration in urban search and rescue environments. An uncertainty evaluation method based on the Fisher Information Matrix (FIM) is studied from the perspective of evaluating the localization uncertainty of SLAM systems. With the aid of the Fisher Information Matrix, the Cramér–Rao Lower Bound (CRLB) of the pose uncertainty in the stereo visual SLAM system is derived to describe the boundary of the pose uncertainty. Optimality criteria are introduced to quantitatively evaluate the localization uncertainty. The odometry information selection method and the local bundle adjustment information selection method based on Fisher Information are proposed to find out the measurements with low uncertainty for localization and mapping in the search and rescue process. By adopting the method above, the computing efficiency of the system is improved while the localization accuracy is equivalent to the classical ORB-SLAM2. Moreover, by the quantified uncertainty of local poses and map points, the generalized unary node and generalized unary edge are defined to improve the computational efficiency in computing local state uncertainty. In addition, an active loop closing planner considering local state uncertainty is proposed to make use of uncertainty in assisting the space exploration and decision-making of MAV, which is beneficial to the improvement of MAV localization performance in search and rescue environments. Simulations and field tests in different challenging scenarios are conducted to verify the effectiveness of the proposed method. Full article

With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower layer path planning stage, considering the limitations of the whale optimization algorithm (WOA), such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. For upper layer task assignment, the improved ALNS (IALNS) addresses local optima issues in complex constraints. It integrates K-means clustering for initialization and a simulated annealing mechanism, improving scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. Full article

The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations. This study proposes a method for vital sign detection using an integrated sensing and communication (ISAC) system where a unified orthogonal frequency division multiplexing (OFDM) signal was adopted, and it is capable of sensing life signs and carrying out communication simultaneously. An ISAC demonstration system based on software-defined radios (SDRs) was initiated to detect respiratory and heartbeat rates while maintaining communication capability in a typical office environment. The specially designed OFDM signals were transmitted, reflected from a human subject, received, and processed to estimate the micro-Doppler effect induced by the breathing and heartbeat of the human in the environment. According to the results, vital signs, including respiration and heartbeat rates, have been accurately detected by post-processing the reflected OFDM signals with a 1 MHz bandwidth, confirmed with conventional contact-based detection approaches. The potential of dual-function capability of OFDM signals for sensing purposes has been verified. The principle and method developed can be applied in wider ISAC systems for search and rescue purposes while maintaining communication links. Full article

In recent years, autonomous driving technology has made remarkable progress in urban transportation and logistics, while its application in complex off-road environments has gradually become a research hotspot. Compared to traditional manned vehicles, unmanned vehicles demonstrate higher safety and flexibility in scenarios such as rapid transportation, emergency rescue, and environmental reconnaissance. However, current research on path planning is predominantly focused on structured environments, with limited attention given to unstructured off-road conditions. This paper proposes an improved A* algorithm tailored to address the challenges of path planning in complex off-road environments. First, a grid map incorporating multi-dimensional information is constructed by integrating elevation data, risk zones, and surface attributes, significantly enhancing environmental perception accuracy. At the algorithm level, the heuristic function and search strategy of the A* algorithm are optimized to improve its efficiency and path smoothness in complex terrains. Furthermore, the method supports the flexible planning of three types of paths—minimizing time, minimizing risk, or optimizing smoothness—based on specific task requirements. Simulation results demonstrate that the improved A* algorithm effectively adapts to dynamic off-road environments, providing intelligent and efficient path planning solutions for unmanned vehicles. The proposed method holds significant value for advancing the application of autonomous driving technology in complex environments. Full article

In response to the issues of unreachable targets, local minima, and insufficient real-time performance in drone path planning in urban low-altitude complex scenarios, this paper proposes a fusion algorithm based on digital twin, integrating LSTM (long short-term memory), DQN (Deep Q-Network), and APF (artificial potential field). The algorithm relies on a twin system, integrating multi-sensor fusion technology and Kalman filtering to input obstacle information and UAV trajectory predictions into the DQN, which outputs action decisions for intelligent obstacle avoidance. Additionally, to address the blind search problem in trajectory planning, the algorithm introduces exploration rewards and heuristic reward components, as well as adding velocity and acceleration compensation terms to the attraction and repulsion functions, reducing the path deviation of UAVs during dynamic obstacle avoidance. Finally, to tackle the issues of insufficient training sample size and simulation accuracy, this paper leverages a digital twin platform, utilizing a dual feedback mechanism from virtual and physical environments to generate a large number of complex urban scenario samples. This approach effectively enhances the diversity and accuracy of training samples while significantly reducing the experimental costs of the algorithm. The results demonstrate that the LSTM-DQN-APF algorithm, combined with the digital twin platform, can significantly improve the issues of unreachable goals, local optimality, and real-time performance in UAV operations in complex environments. Compared to traditional algorithms, it notably enhances path planning speed and obstacle avoidance success rates. After thorough training, the proposed improved algorithm can be applied to real-world UAV systems, providing reliable technical support for applications such as smart city inspections and emergency rescue operations. Full article

Navigation for autonomous robots in hazardous environments demands robust localization solutions. In challenging environments such as tunnels and urban disaster areas, autonomous robots and vehicles are particularly important for search and rescue operations. However, especially in these environments, sensor failures and errors make the localization task particularly difficult. We propose a robust sensor fusion algorithm that integrates data from a thermal camera, a LiDAR sensor, and a GNSS to provide reliable localization, even in environments where individual sensor data may be compromised. The thermal camera and LiDAR sensor employ distinct SLAM and odometry techniques to estimate movement and positioning, while an extended Kalman filter (EKF) fuses all three sensor inputs, accommodating varying sampling rates and potential sensor outages. To evaluate the algorithm, we conduct a field test in an urban environment using a vehicle equipped with the appropriate sensor suite while simulating an outage one at a time, to demonstrate the approach’s effectiveness under real-world conditions. Full article

Deep Reinforcement Learning (DRL) has emerged as a transformative approach in mobile robot path planning, addressing challenges associated with dynamic and uncertain environments. This comprehensive review categorizes and analyzes DRL methodologies, highlighting their effectiveness in navigating high-dimensional state–action spaces and adapting to complex real-world scenarios. The paper explores value-based methods like Deep Q-Networks (DQNs) and policy-based strategies such as Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), emphasizing their contributions to efficient and robust navigation. Hybrid approaches combining these methodologies are also discussed for their adaptability and enhanced performance. Additionally, the review identifies critical gaps in current research, including limitations in scalability, safety, and generalization, proposing future directions to advance the field. This work underscores the transformative potential of DRL in revolutionizing mobile robot navigation across diverse applications, from search-and-rescue missions to autonomous urban delivery systems. Full article

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios. Full article

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance. Full article

Autonomous robotic teams have been proposed for a variety of lost-person searches in wilderness and urban settings. In the latter scenarios, for missing persons, the application of such teams, however, is more challenging than it would be in the wilderness. This paper, specifically, examines the application of an autonomous team of unmanned aerial vehicles (UAVs) to perform a sparse, mobile-target search in an urban setting. A novel multi-UAV search-trajectory planning method, which relies on the prediction of the missing-person’s motion, given a known map of the search environment, is the primary focus. The proposed method incorporates periodic updates of the estimates of where the lost/missing person may be, allowing for intelligent re-coverage of previously searched areas. Additional significant contributions of this work include a behavior-based motion-prediction method for missing persons and a novel non-parametric estimator for iso-probability-based (missing-person-location) curves. Simulated experiments are presented to illustrate the effectiveness of the proposed search-planning method, demonstrating higher rates of missing-person detection and in shorter times compared to other methods. Full article

Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions. Full article

Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor. Automated navigation from outdoor to indoor environments is not trivial, as it encompasses the ability of a UAV to automatically map and locate the openings in a damaged building. This study focuses on developing a deep learning model for the detection of damaged building openings in real time. A novel damaged building-opening dataset containing images and mask annotations, as well as a comparison between single and multi-task learning-based detectors are given. The deep learning-based detector used in this study is based on YOLOv5. First, this study compared the different versions of YOLOv5 (i.e., small, medium, and large) capacity to perform damaged building-opening detections. Second, a multitask learning YOLOv5 was trained on the same dataset and compared with the single-task detector. The multitask learning (MTL) was developed based on the YOLOv5 object detection architecture, adding a segmentation branch jointly with the detection head. This study found that the MTL-based YOLOv5 can improve detection performance by combining detection and segmentation losses. The YOLOv5s-MTL trained on the damaged building-opening dataset obtained 0.648 mAP, an increase of 0.167 from the single-task-based network, while its inference speed was 73 frames per second on the tested platform. Full article

Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the environment. The environment can generate occlusion, complicating the timely detection of people. There are currently numerous RGB image datasets available that are used for person detection tasks in urban and wooded areas and consider the general characteristics of a person, like size, shape, and height, without considering the occlusion of the object of interest. The present research work focuses on developing a thermal image dataset, which considers the occlusion situation to develop CNN convolutional deep learning models to perform detection tasks in real-time from an aerial perspective using altitude control in a quadcopter prototype. Extended models are proposed considering the occlusion of the person, in conjunction with a thermal sensor, which allows for highlighting the desired characteristics of the occluded person. Full article

Understanding the VOC profile released during the early post-mortem period is essential for applications in training human remains detection dogs and urban search and rescue operations (USAR) to rapidly locate living and deceased victims. Human cadavers were sampled at the UQTR morgue within a 0–72 h post-mortem interval. VOC samples were collected from the headspace above the cadavers, using Tenax TA/Carbograph 5TD dual sorbent tubes, and analyzed using GC×GC-TOFMS. Multiple data processing steps, including peak table alignment and filtering, were undertaken using LECO ChromaToF and custom scripts in R programming language. This study identified 104 prevalent VOCs, some of which are linked to human decomposition, while others are connected to the persistence of living scent. Principal Component Analysis (PCA) further highlighted that VOC profiles can change dynamically over time, even in a controlled setting. The findings underscore the complexity and variability in VOC profiles during the early post-mortem period. This variability is influenced by multiple factors including the individual’s biological and physiological conditions. Despite the challenges in characterizing these profiles, the identified VOCs could potentially serve as markers in forensic applications. The study also highlights the need for additional research to build a dataset of VOCs for more robust forensic applications. Full article

To deploy Unmanned Aerial Vehicles (UAVs) inside heterogeneous GPS-denied confined (potentially unknown) spaces, such as those encountered in mining and Urban Search and Rescue (USAR), requires the enhancement of numerous technologies. Of special interest is for UAVs to identify collision-freeSafe Flight Corridors (SFC${}^{+}$) within highly cluttered convex- and non-convex-shaped environments, which requires UAVs to perform advanced flight maneuvers while exploiting their flying capabilities. Within this paper, a novel auxiliary occupancy checking process that augments traditional 3D flight corridor generation is proposed. The 3D flight corridor is established as a topological structure based on a hand-crafted path either derived from a computer-generated environment or provided by the human operator, which captures humans’ preferences and desired flight intentions for the given space. This corridor is formulated as a series of interconnected overlapping convex polyhedra bounded by the perceived environmental geometries, which facilitates the generation of suitable 3D flight paths/trajectories that avoid local minima within the corridor boundaries. An occupancy check algorithm is employed to reduce the search space needed to identify 3D obstacle-free spaces in which their constructed polyhedron geometries are replaced with alternate convex polyhedra. To assess the feasibility and efficiency of the proposed SFC${}^{+}$ methodology, a comparative study is conducted against the Star-Convex Method (SCM), a prominent algorithm in the field. The results reveal the superiority of the proposed SFC${}^{+}$ methodology in terms of its computational efficiency and reduced search space for UAV maneuvering solutions. Various challenging confined-environment scenarios, each with different obstacle densities (confined scenarios), are utilized to verify the obtained outcomes. Full article