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"Rescue operations"
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Saving Bravo : the greatest rescue mission in Navy SEAL history
\"The untold story of the most important rescue mission not just of the Vietnam War, but the entire Cold War: one American aviator, who knew our most important secrets, crashed behind enemy lines and was sought by the entire North Vietnamese and Russian military machines. One Navy SEAL and his Vietnamese partner had to sneak past them all to save him\"-- Provided by publisher.
Book
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.
Journal Article
HH-60 Pave Hawk helicopters
The United States Air Force uses HH-60 Pave Hawks to deploy Special Forces and to perform search-and-rescue missions. Simple text and photos highlight how the speed and technology of these helicopters help them complete missions for the U.S. Air Force.
Book
Enhanced YOLO11 for lightweight and accurate drone-based maritime search and rescue object detection
Accurately and rapidly detecting objects and their locations in drone-captured images from maritime search and rescue scenarios provides valuable information for rescue operations. The YOLO series, known for its balance between lightweight architecture and high accuracy, has become a popular method among researchers in this field. Recent advancements in the newly released YOLO11 model have demonstrated significant progress in general object detection tasks across everyday scenarios. However, its application to the specific task of drone-based maritime search and rescue still leaves substantial room for improvement. To address this gap, we propose targeted optimizations to enhance YOLO11’s performance in this domain. These include integrating a Space-to-Depth module into the Backbone, incorporating a content-aware upsampling algorithm in the Neck, and adding an extra detection head to better exploit shallow image features. These modifications significantly improve the model’s ability to detect small, overlapping, and rarely occurring objects, which are common challenges in maritime search and rescue tasks. Experimental evaluations conducted on the large-scale SeaDronesSee dataset demonstrate that the proposed optimized YOLO11 outperforms YOLOv8, YOLO11, and MambaYOLO across all scales. Moreover, under lightweight configurations, the model achieves substantial performance gains over YoloOW, a method renowned for its accuracy but depends on heavyweight configurations. In the lightweight complexity range, the proposed model achieves a relative accuracy improvement of 20.85% to 43.70% compared to these state-of-the-art methods. The code supporting this research is available at https://github.com/bgno1/sds_yolo11 .
Journal Article
The Post-Disaster Debris Clearance Problem Under Incomplete Information
Debris management is one of the most time consuming and complicated activities among post-disaster operations. Debris clearance is aimed at pushing the debris to the sides of the roads so that relief distribution and search-and-rescue operations can be maintained in a timely manner. Given the limited resources, uncertainty, and urgency during disaster response, efficient and effective planning of debris clearance to achieve connectivity between relief demand and supply is important. In this paper, we define the stochastic debris clearance problem (SDCP), which captures post-disaster situations where the limited information on the debris amounts along the roads is updated as clearance activities proceed. The main decision in SDCP is to determine a sequence of roads to clear in each period such that benefit accrued by satisfying relief demand is maximized. To solve SDCP to optimality, we develop a partially observable Markov decision process model. We then propose a heuristic based on a continuous-time approximation, and we further reduce the computational burden by applying a limited look ahead on the search tree and heuristic pruning. The performance of these approaches is tested on randomly generated instances that reflect various geographical and information settings, and instances based on a real-world earthquake scenario. The results of these experiments underline the importance of applying a stochastic approach and indicate significant improvements over heuristics that mimic the current practice for debris clearance.
Journal Article
Stella the search dog
\"Meet courageous canine Stella and follow along as she spends an exciting day at work with the police department ... [Tells] the true stories of brave Stella's role as a search dog in Virginia\"-- Provided by publisher.
Book
“It’s my calling”, Canadian dog rescuers’ motives and experiences for engaging in international dog rescue efforts
The importation of rescue dogs has become an increasingly common occurrence in recent years, often involving industrialized countries as the ultimate destination. However, international dog rescue activities have attracted considerable criticism from the veterinary community and the public due to the associated zoonotic disease and public health risks, as well as the potential poor behaviour of international rescue dogs. The Government of Canada has also recently placed a temporary suspension on all commercial dog imports from non-rabies free countries due to the growing concerns of the zoonotic disease risks. To understand the perspectives and experiences of stakeholders involved in dog import activities in Canada, we interviewed nine members in leadership positions of Canadian-based international dog rescue organizations. Thematic analysis of interview dialogue yielded three themes: 1)
Motive
, which described the reason behind participants’ involvement in international dog rescue; 2)
Challenge
, which described the major difficulties faced in participants’ dog rescue work; 3)
Duty
, which described participants’ beliefs on responsible dog rescue practices. Members of international dog rescue organizations described being driven by strong desires to provide animal and humanitarian aid. However, local dog rescue efforts were constrained by logistical and societal barriers unique to the Canadian context. Additionally, the current study revealed both similarities and differences in occupational experiences between international dog rescue organizations and other animal care professions. Specifically, difficulties with the emotional burden associated with caregiving professions was also present within dog rescue work. However, international dog rescue members also experienced additional challenges due to the stigma surrounding international dog rescue operations. Further research on attitudes held by other stakeholders involved in dog import activities, as well as members of Canadian communities needing dog rescue aid may provide meaningful inputs on how to better support and facilitate local and international dog rescue efforts.
Journal Article
AntBot-EX: Enhancing robot search efficiency in complex post-disaster environments
In post-disaster scenarios, effective rescue operations hinge on deploying robots equipped with sophisticated path planning algorithms capable of navigating through complex and unknown environments, facilitating an exhaustive search for survivors. The inherent limitations of traditional Coverage Path Planning (CPP) algorithms, particularly their struggle to adapt to the highly dynamic and unpredictable nature of post-disaster environments characterized by collapsed structures, shifting debris fields, and unforeseen obstacles, hinder their effectiveness in time-sensitive rescue operations. To address the challenges, this paper introduces an innovative three-stage online CPP method, termed Ant Colony Optimization based Robot Exploration with Escape Mechanism (AntBot-EX). Our three-stage approach leverages the strengths of different algorithms. Firstly, we utilize a modified Ant Colony Optimization algorithm to explore the unknown environment efficiently, prioritizing uncharted territories and avoiding potential dead ends using an escape mechanism. Secondly, the remaining unexplored areas are segmented, enabling targeted path planning with the A * algorithm to maximize coverage. Thirdly, to address computational limitations in large and complex environments, a configurable boundary-aware and a score-based threshold are introduced to simplify paths by strategically disregarding irrelevant regions, optimizing search efficiency. Simulation results show that our method can basically achieve complete coverage in complex and unknown environments.
Journal Article