Rescue Domain Research Articles

An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images

Unmanned aerial vehicles (UAVs), renowned for their rapid deployment, extensive data collection, and high spatial resolution, are crucial in locating distressed individuals during search and rescue (SAR) operations. Challenges in maritime search and rescue include missed detections due to issues including sunlight reflection. In this study, we proposed an enhanced ABT-YOLOv7 algorithm for underwater person detection. This algorithm integrates an asymptotic feature pyramid network (AFPN) to preserve the target feature information. The BiFormer module enhances the model’s perception of small-scale targets, whereas the task-specific context decoupling (TSCODE) mechanism effectively resolves conflicts between localization and classification. Using quantitative experiments on a curated dataset, our model outperformed methods such as YOLOv3, YOLOv4, YOLOv5, YOLOv8, Faster R-CNN, Cascade R-CNN, and FCOS. Compared with YOLOv7, our approach enhances the mean average precision (mAP) from 87.1% to 91.6%. Therefore, our approach reduces the sensitivity of the detection model to low-lighting conditions and sunlight reflection, thus demonstrating enhanced robustness. These innovations have driven advancements in UAV technology within the maritime search and rescue domains.

The State of Business Rescue Practitioners Professional Accreditation

Business rescue practitioners (BRPs) are subject to many allegations of abuse and, therefore, professional accreditation has become a pre-requisite. The Companies and Intellectual Property Commission (CIPC) licensing is linked to multiple professional bodies’ knowledge and practices but is not generic. This study was guided by one key question: What is BRPs’ accreditation’s current state in a multiple professional body occupation? We used data mapped to scholarly and documented policy sources, categorized results from extensive reading, and integrated critical constructs (after the deconstruction of concepts) to yield a conceptual framework to develop a comprehensive understanding of professional accreditation. The results confirm the existence of a legal framework and institutional arrangements that are not coherently applied because of the absence of a professional accreditation framework (PAF). The proposed conceptual framework captures the concepts of the business rescue domain, professionalism, competency, accreditation, and definition of key terms to provide an interpretive approach to the BRPs’ accreditation reality resulting in a PAF based on the integration of BRP tasks and services and accreditation, competency, and professionalism.

A distributed approach for road clearance with multi-robot in urban search and rescue environment

In urban search and rescue domains, robots explore the affected terrain to search and assist disaster victims. RoboCup Rescue simulation provides a platform for disaster management where heterogeneous field agents (fire-brigade agent, ambulance agent, and police force agent) collaborate to manage a mimicked calamity situation. The role of police force agents is crucial, as they clear the blocked roads to allow other agents to perform their tasks. In this paper we suggest a distributed multi-robot coordination approach for clearing a road blocked with heavy obstacles. The proposed framework is implemented and simulated in ARGoS, a multi-robot simulator. The experimental results show the validity and satisfactory performance of the proposed approach.

How Good are Distributed Allocation Algorithms for Solving Urban Search and Rescue Problems? A Comparative Study With Centralized Algorithms

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO. Note to Practitioners —This paper was motivated by the limitation of current distributed task allocation algorithms as they cannot achieve performances that are as good as the centralized ones. Therefore, a centralized algorithm is designed to evaluate the performance gap between the state-of-the-art distributed and centralized approaches. In the future research section, a new distributed particle swarm optimization (PSO) algorithm is proposed based on this paper as the research has shown that the proposed centralized PSO algorithm delivers the best results so it is potentially a strong candidate for adaptation.

A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications

Abstract The team-orienteering problem (TOP) has broad applicability. Examples of possible uses are in factory and automation settings, robot sports teams, and urban search and rescue applications. We chose the rescue domain as a guiding example throughout this paper. Hence, this paper explores a practical variant of TOP with time window (TOPTW) for rescue applications by humanoid robots called TOPTWR. Due to the significant range of algorithm choices and their parameters tuning challenges, the use of hyper-heuristics is recommended. Hyper-heuristics can select, order, or generate different low-level heuristics with different optimization algorithms. In this paper, first, a general multi-objective (MO) solution is defined, with five objectives for TOPTWR. Then a robust and efficient MO and evolutionary hyper-heuristic algorithm for TOPTW based on the humanoid robot’s characteristics in the rescue applications (MOHH-TOPTWR) is proposed. MOHH-TOPTWR includes two MO evolutionary metaheuristics algorithms (MOEAs) known as non-dominated sorting genetic algorithm (NSGA-III) and MOEA based on decomposition (MOEA/D). In this paper, new benchmark instances are proposed for rescue applications using the existing ones for TOPTW. The experimental results show that MOHH-TOPTWR in both MOEAs can outperform all the state-of-the-art algorithms as well as NSGA-III and MOEA/D MOEAs.

Hierarchical task network-based emergency task planning with incomplete information, concurrency and uncertain duration

This paper focuses on the emergency task planning problem with the following characteristics: incomplete initial environment information, concurrent execution and uncertain execution durations. To address this problem, the conditional temporal hierarchical task network (HTN) planning with uncertain durations should be investigated. However, the research work on conditional HTN planning mainly concentrates on the planning domain without temporal features and pays no attention to the handling of concurrency and uncertain durations. This motivates us to propose a planning paradigm based on the conditional HTN planning. The planning paradigm incorporates a temporal reasoning technique suitable for conditional HTN planning to achieve concurrency and a temporal management approach to confirm the satisfiability of temporal constraints efficiently. For temporal reasoning, rules for generating temporal constraints are designed to avoid three types of flaws due to interactions between durative actions; a mechanism for detecting invalid temporal constraints is also presented. Regarding temporal management, Conditional Simple Temporal Network with Uncertainty (CSTNU) is utilized to represent the temporal constraints involving uncontrollable intervals and observations, and an approach for eliminating redundant temporal constraints is proposed to reduce the temporal constraint to be represented in a CSTNU to promote efficiency. Finally, an experimental study of an earthquake rescue domain demonstrates the viability of the proposed planning paradigms.

음성인식 기반 응급상황관제

In emergency dispatching at 119 Command & Dispatch Center, some inconsistencies between the 'standard emergency aid system' and 'dispatch protocol,' which are both mandatory to follow, cause inefficiency in the dispatcher's performance. If an emergency dispatch system uses automatic speech recognition (ASR) to process the dispatcher's protocol speech during the case registration, it instantly extracts and provides the required information specified in the 'standard emergency aid system,' making the rescue command more efficient. For this purpose, we have developed a Korean large vocabulary continuous speech recognition system for 400,000 words to be used for the emergency dispatch system. The 400,000 words include vocabulary from news, SNS, blogs and emergency rescue domains. Acoustic model is constructed by using 1,300 hours of telephone call (8 kHz) speech, whereas language model is constructed by using 13 GB text corpus. From the transcribed corpus of 6,600 real telephone calls, call logs with emergency rescue command class and identified major symptom are extracted in connection with the rescue activity log and National Emergency Department Information System (NEDIS). ASR is applied to emergency dispatcher's repetition utterances about the patient information. Based on the Levenshtein distance between the ASR result and the template information, the emergency patient information is extracted. Experimental results show that 9.15% Word Error Rate of the speech recognition performance and 95.8% of emergency response detection performance are obtained for the emergency dispatch system.

Dynamic task allocation for multi-robot search and retrieval tasks

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. For example, in a search and rescue domain, a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing such a search and retrieval task by more than one robot increases performance if they are able to distribute their workload efficiently and evenly. In this work, we study the Multi-Robot Task Allocation (MRTA) problem in the search and retrieval domain, where a team of robots is required to cooperatively search for targets of interest in an environment and also retrieve them back to a home base. In comparison with typical foraging tasks, we look at a more general search and retrieval task in which the targets are distinguished with various types, and task allocation also requires taking into account temporal constraints on the team goal. As usual, robots have no prior knowledge about the location of targets in the environment but in addition they need to deliver targets to the home base in a specific order according to their types, which significantly increases the complexity of a foraging problem. We first use a graph-based model to analyse the search and retrieval problem and the dynamics of exploration and retrieval within a cooperative team. We then proceed to present an extended auction-based approach, as well as a prediction approach. The essential difference between these two approaches is that the task allocation and execution procedures in the auction approach are running in parallel, whereas a robot in the prediction approach only needs to choose a task to perform when it has no thing to do. The auction approach uses a winner determination mechanism to allocate tasks to each robot, whereas the robots in the prediction approach implicitly coordinate their activities by team reasoning that leads to consensuses about task allocation. We use the Blocks World for Teams (BW4T) simulator to evaluate the two approaches in our experimental study.

Nearest Adjacent Node Discovery Scheme for Routing Protocol in Wireless Sensor Network

The broad significance of Wireless Sensor Networks is in most emergency and disaster rescue domain. The routing process is the main challenges in the wireless sensor network due to lack of physical links. The objective of routing is to find optimum path which is used to transferring packets from source node to destination node. Routing should generate feasible routes between nodes and send traffic along the selected path and also achieve high performance. This paper presents a nearest adjacent node scheme based on shortest path routing algorithm. It is plays an important role in energy conservation. It finds the best location of nearest adjacent nodes by involving the least number of nodes in transmission of data and set large number of nodes to sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and enhance the life of the network.

Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents

Previous approaches to select agents to form a team rely on single-agent capabilities, and team performance is treated as a sum of such known capabilities. Motivated by complex team formation situations, we address the problem where both single-agent capabilities may not be known upfront, e.g., as in ad hoc teams, and where team performance goes beyond single-agent capabilities and depends on the specific synergy among agents. We formally introduce a novel weighted synergy graph model to capture new interactions among agents. Agents are represented as vertices in the graph, and their capabilities are represented as Normally-distributed variables. The edges of the weighted graph represent how well the agents work together, i.e., their synergy in a team. We contribute a learning algorithm that learns the weighted synergy graph using observations of performance of teams of only two and three agents. Further, we contribute two team formation algorithms, one that finds the optimal team in exponential time, and one that approximates the optimal team in polynomial time. We extensively evaluate our learning algorithm, and demonstrate the expressiveness of the weighted synergy graph in a variety of problems. We show our approach in a rich ad hoc team formation problem capturing a rescue domain, namely the RoboCup Rescue domain, where simulated robots rescue civilians and put out fires in a simulated urban disaster. We show that the weighted synergy graph outperforms a competing algorithm, thus illustrating the efficacy of our model and algorithms.

Outlier Detection by Interaction with Domain Experts

We present a method for improving the detection of outlying Fire Service's reports based on domain knowledge and dialogue with Fire & Rescue domain experts. The outlying report is considered as an element which is significantly different from the remaining data. We follow the position of Professor Andrzej Skowron that effective algorithms in data mining and knowledge discovery in big data should incorporate an interaction with domain experts or/and be domain oriented. Outliers are defined and searched on the basis of domain knowledge and dialogue with experts. We face the problem of reducing high data dimensionality without loosing specificity and real complexity of reported incidents. We solve this problem by introducing a knowledge based generalization level intermediating between analyzed data and experts domain knowledge. In our approach we use the Formal Concept Analysis methods for both generation of the appropriate categories from data and as tools supporting communication with domain experts. We conducted two experiments in finding two types of outliers in which outlier detection was supported by domain experts.

Controlled balance losing in random step environment for path planning of a teleoperated crawler‐type vehicle

AbstractRescue robotics is the application of robotics to the search and rescue domain, aimed at extending the capabilities and increasing the safety of the rescuers. Deployed on a site during a rescue mission, a mobile robot is teleoperated by a human operator from a safe place. To suggest to the operator a good direction to traverse the three‐dimensional (3D) debris environment, we develop a pilot system, which requires a special path search algorithm on debris and a proper definition of a search tree. Although the main goal of the algorithm is to keep the robot maximally stable at every step of its path, in some cases we need the robot to change a 3D orientation discontinuously through losing its balance. Losing balance on purpose is an essential feature for safe climbing up and going down debris, and it is the central issue of this paper. Exhaustive simulations were used to structure and analyze data. Experiments with a real robot verified our approach to removing unsuitable search directions from the search tree and gave important feedback to the algorithm. © 2011 Wiley Periodicals, Inc.

Evolution and field performance of a rescue robot

AbstractRobots are slowly finding their way into the hands of search and rescue groups. One of the robots contributing to this effort is the Inuktun VGTV‐Xtreme series by American Standard Robotics. This capable robot is one of the only robots engineered specifically for the search and rescue domain. This paper describes the adaptation of the VGTV platform from an industrial inspection robot into a capable and versatile search and rescue robot. These adaptations were based on growing requirements established by rescue groups, academic research, and extensive field trials. A narrative description of a successful search of a damaged building during the aftermath of Hurricane Katrina is included to support these claims. Finally, lessons learned from these deployments and guidelines for future robot development is discussed. © 2008 Wiley Periodicals, Inc.

Network transparency in a mountain rescue domain

PurposeThis paper aims to discuss network transparency in a mountain rescue domain and aims to introduce the relatively new research concept of MANEMO (MANET + NEMO) and the mountain rescue IP network model developed at Lancaster University.Design/methodology/approachAn analysis of IP mobility techniques that have been designed for use in the Internet today is provided as background, as well as an overview of the whole MANEMO domain. Highlights the importance of the MANEMO concept by working with real scenarios and developing practical implementations (as opposed to simulation).FindingsDuring the research it was found that combining the localised multi‐hop behaviour of MANET techniques and the global reachability of NEMO can be a mutually beneficial process. The benefits afforded by using MANET techniques can bring advantages to NEMO scenarios and vice‐versa. Identifying this fact has given rise to the development of two distinct MANEMO scenarios, MANET‐Centric and NEMO‐Centric MANEMO. Finally, the Unified MANEMO Architecture (UMA) implementation, which attempts to support both of these scenarios in an efficient and feasible manner.Research limitations/implicationsRegarding UMA, research continues into addressing the security implications of this approach and the benefits that multi‐homing can provide.Originality/valueThe paper illustrates how MANEMO can conceivably provide powerful solutions to many important scenarios.

Evolving interface design for robot search tasks

AbstractThis paper describes two steps in the evolution of human‐robot interaction designs developed by the University of Massachusetts Lowell (UML) and the Idaho National Laboratory to support urban search and rescue tasks. Usability tests were conducted to compare the two interfaces, one of which emphasized three‐dimensional mapping while the other design emphasized the video feed. We found that participants desired a combination of the interface design approaches. As a result, the UML system was changed to augment its heavy emphasis on video with a map view of the area immediately around the robot. The changes were tested in a follow‐up user study and the results from that experiment suggest that performance, as measured by the number of collisions with objects in the environment and time on task, is better with the new interaction techniques. Throughout the paper, we describe how we applied human‐computer interaction principles and techniques to benefit the evolution of the human‐robot interaction designs. While the design work is situated in the urban search and rescue domain, the results can be generalized to domains that involve other search or monitoring tasks using remotely located robots. © 2007 Wiley Periodicals, Inc.

Best multiple-view selection for the visualization of urban rescue simulations

In this paper we address the problem of automatically computing a set of views over a simulated three dimensional environment. The viewing system aims at, for each moment, supplying the user with the most pertinent information in order to allow a good understanding of the evolving environment. Our approach relies on an innovative optimization architecture that enables intelligent optimization techniques based on simulated annealing and genetic algorithms. Reported experiments were performed in urban rescue scenarios from the RoboCup Rescue Domain. We outline the possible extension of the proposed architecture to other visualization problems and argue on how several problems within the fields of Visualization and Rendering can benefit from it.

FC PORTUGAL: DEVELOPMENT AND EVALUATION OF A NEW ROBOCUP RESCUE TEAM

FC Portugal Rescue team is the result of a cooperation project between the Universities of Aveiro and Porto in Portugal. Following previous collaborations of these two Portuguese Universities in RoboCup simulation league and associated competitions, this project intends to fully adapt the coordination methodologies developed by FC Portugal simulated soccer team to the search and rescue scenario. After FC Portugal's first successful participation in RoboCup rescue simulation league (Osaka, 2005), the team aimed at introducing new high-level strategic analysis tools in the Rescue league and developed FCPx tool. This tool enables an easy comparison of different teams' strategies, and the use of learning methodologies for high-level strategic decisions in rescue. The tool also enabled us to evaluate our coordination methodologies, analysing the results achieved in several rescue parameters, and conclude that they are very promising for the search and rescue domain. FC Portugal Rescue team results are very promising achieving first place in RoboCup European Championship 2006 (Dutch Open) held in Eindhoven.

Rescue MIKE: An Automatic Commentary System for Disaster Simulation System design and implementation of version 0.

We designed the system, Rescue-MIKE, and implemented to the RoboCup Rescue Simulation System. The Rescue MIKE system aims to communicate with large numbers of relief workers and controllers working in a rescue domain. The system has an ability to report the situation of the domain like a newscaster. Our system consists of multiple agents. The system collects information from a simulated disaster scenario, and then produces a dialogue that fits to the actions of the agents in the domain. We described the design policy, implementation of our system, and Rescue-MIKE's facility. Rescue-MIKE initial version can provide aural information that helps to explain the unfolding scenarios to onlookers. We discussed the future applications of our system, including knowledge elicitation about disaster relief control methods, automated relief support systems, and public education about the dangers of large natural disasters such as earthquakes, floods or volcanic eruptions.

AGENT-BASED SUPPORT FOR BALANCING TELEOPERATION AND AUTONOMY IN URBAN SEARCH AND RESCUE

Today's artificial intelligence technology is insufficient to support autonomous task performance in complex domains such as urban search and rescue, forcing extensive reliance on human teleoperation of robots. This, however, also has problems: humans quickly suffer from cognitive overload and have difficulties in constructing a representation of the space around a remotely placed robot. In this paper we describe an approach to multirobot control for such environments that focuses on combining the limited abilities of modern autonomous control systems together with human control. At the centre of this approach is a pair of software agents running on each robot: one to recognize problems in the environment from the perspective of a robot, and another to mediate the interaction between a robot and a human controller. The intent of this approach is to allow a human to better control a team of robots, being interrupted only when the situation demands. We describe the implementation of this approach using simulated Pioneer robots, and evaluate the approach in comparison to autonomous and teleoperated mobile robots in a rescue domain.

RoboCup rescue: A grand challenge for multiagent and intelligent systems

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.