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Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario. In this study, we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem (TSP) with life-strength constraints. To address this problem, we proposed an improved iterated greedy (IIG) algorithm. First, a push-forward insertion heuristic (PFIH) strategy was employed to generate a high-quality initial solution. Second, a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm's exploration ability. Furthermore, three problem-specific swap operators were developed to improve the algorithm's exploitation ability. Additionally, an improved simulated annealing (SA) strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima. To verify the effectiveness of the proposed algorithm, the Solomon dataset was extended to generate 27 instances for simulation. Finally, the proposed IIG was compared with five state-of-the-art algorithms. The parameter analysis was conducted using the design of experiments (DOE) Taguchi method, and the effectiveness analysis of each component has been verified one by one. Simulation results indicate that IIG outperforms the compared algorithms in terms of the number of rescue survivors and convergence speed, proving the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to enable rescue robots to accurately detect human joint points in dangerous building environments to replace rescuers into dangerous scenes. A machine learning based human joint point detection method was proposed. Firstly, human joint detection data was obtained through Kinect cameras, and normalized methods were used to smooth and preprocess the joint data. Secondly, human joint length constraints and joint rotation angle constraints were added to the preprocessed data to generate the spatial positions of each human joint point. Finally, the Tsai method was used for hand eye calibration to obtain the distance between the robotic arm and the injured person. The experimental results show that the method accurately measures the position of the casualty joint point in the rescue environment of a dangerous building, which verifies the effectiveness of the proposed detection method. [ABSTRACT FROM AUTHOR]

Natural calamities are affecting many parts of the world. Natural disasters, terrorist attacks, earthquakes, wildfires, floods and all unpredicted phenomena. Disasters cause emergency conditions, so imperative to coordinate the prompt delivery of essential services to the sufferers. Often, disasters lead many people to perish by becoming trapped inside, but many more also perish as a result of individuals receiving rescue either too late or not at all. The implementation and design of a Receiver module utilizing Davinci code processor DVM6437, Wireless camera receiver, Zigbee Transceiver and Global Positioning System (GPS) is proposed in this manuscript for Wireless Vision-based Semi-Autonomous rescue robots that are employed in rough terrain. The receiver side's Zigbee transceiver module eliminates the limitations of tele-operating rescue robots by enabling the control station to receive GPS data signals and aids in robot management by sending control signals wirelessly. Half and full-duplex communication are supported by the Davinci processor DVM6437, a digital media fixed-point DSP processor that relies on Very Long Instruction Words. It includes an extensive instruction set that is ideal for real-time salvage operations. DVM processor is coded utilizing MATLAB Simulink. MATLAB codes and Simulink blocks are employed under Embedded IDE link. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the development status, pain points and future trend of China’s coal mine robot industry. In the context of the “robot +” era, this paper expounds the development of China’s robot industry from the aspects of the number of robot enterprises, regional distribution, industrial park construction, advantage track, national policy, etc., and expounds the changes and challenges brought by the domestic Internet and manufacturing enterprises. This paper focuses on the analysis of coal mine robot technology and industry pain points, and extracts three major capabilities that coal mine robot should have to realize intelligence. Strengthening the construction of supply chain and standard system is proposed. The classification standard of coal mine robot L0-L5 is introduced in detail.

Tracked robots equipped with flippers and LiDAR sensors have been widely used in urban search and rescue. Achieving autonomous flipper control is important in enhancing the intelligent operation of tracked robots within complex urban rescuing environments. While existing methods mainly rely on the heavy work of manual modeling, this paper proposes a novel Deep Reinforcement Learning (DRL) approach named ICM-D3QN for autonomous flipper control in complex urban rescuing terrains. Specifically, ICM-D3QN comprises three modules: a feature extraction and fusion module for extracting and integrating robot and environment state features, a curiosity module for enhancing the efficiency of flipper action exploration, and a deep Q-Learning control module for learning robot-control policy. In addition, a specific reward function is designed, considering both safety and passing smoothness. Furthermore, simulation environments are constructed using the Pymunk and Gazebo physics engine for training and testing. The learned policy is then directly transferred to our self-designed tracked robot in a real-world environment for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over hand-crafted control models and state-of-the-art DRL strategies for crossing complex terrains. [ABSTRACT FROM AUTHOR]

This paper proposes a novel design strategy and task‐priority‐based control methodology for a robot to successfully complete a rescue operation in an extremely unstructured environment. The mechanical structure is designed to obtain both versatile manipulability and all‐terrain mobility. The regularized hierarchical quadratic program is used for whole‐body motion and force control. The optimization strategy is reasoning about regularization and thus it ensures convergence of the solution in the face of singularities while taking into account equality and inequality constraints. We demonstrate the effectiveness of the online optimization‐based control algorithms through extensive real‐world numerical and experimental results. Finally, we highlight that the rescue robot can successfully execute missions to extract a casualty and dispose of a dangerous object both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

The underground building environment plays an increasingly important role in the construction of modern cities. To deal with possible fires, collapses, and so on, in underground building space, it is a general trend to use rescue robots to replace humans. This paper proposes a dual‐robot system solution for search and rescue in an underground building environment. To speed up rescue and search, the two robots focus on different tasks. However, the environmental perception information and location of them are shared. The primary robot is used to quickly explore the environment in a wide range, identify objects, cross difficult obstacles, and so on. The secondary robot is responsible for grabbing, carrying items, clearing obstacles, and so on. In response to the difficulty of rescue caused by unknown scenes, the Lidar, inertial measurement unit and multiview cameras are integrated for large‐scale 3D environment mapping. The depth camera detects the objects to be rescued and locate them on the map. A six‐degree‐of‐freedom manipulator with a two‐finger gripper is equipped to open doors and clear roadblocks during the rescue. To solve the problem of severe signal attenuation caused by reinforced concrete walls, corners and long‐distance transmission, a wireless multinode networking solution is adopted. In the case of a weak wireless signal, the primary robot uses autonomous exploration for environmental perception. Experimental results show the robots' system has high reliability in over‐the‐horizon maneuvering, teleoperation of the door opening and grasping, object searching, and environmental perception, and can be well applied to underground search and rescue. [ABSTRACT FROM AUTHOR]

In this study, we develop a novel damper prototype for robot manipulators of a mobile robot to perform rescue operations in extreme environments like battlefields. Shock vibrations occur when such robots drive over rugged terrain, adversely affecting both the robot and the rescued person. We propose a joint module that utilizes magnetorheological (MR) fluid to depress environmental impact vibrations. To do this, we first suggest a novel structure for our MR damper, with multiple working coils to augment the magnetic field intensity for the given volume; then, we perform dynamic simulations on the manipulator with the suggested MR damper installed, examining vertical impact acceleration and the driving torque generated in the joint module. The proposed damper can best reduce the amplitude of vibrations by about 90 % at 21 Hz and by about 30 % at the system's resonant frequency of 22 Hz, which are the frequencies of interest as those lie around the natural frequency of the robot arm. [ABSTRACT FROM AUTHOR]

Tracked robots equipped with flippers and LiDAR sensors have been widely used in urban search and rescue. Achieving autonomous flipper control is important in enhancing the intelligent operation of tracked robots within complex urban rescuing environments. While existing methods mainly rely on the heavy work of manual modeling, this paper proposes a novel Deep Reinforcement Learning (DRL) approach named ICM-D3QN for autonomous flipper control in complex urban rescuing terrains. Specifically, ICM-D3QN comprises three modules: a feature extraction and fusion module for extracting and integrating robot and environment state features, a curiosity module for enhancing the efficiency of flipper action exploration, and a deep Q-Learning control module for learning robot-control policy. In addition, a specific reward function is designed, considering both safety and passing smoothness. Furthermore, simulation environments are constructed using the Pymunk and Gazebo physics engine for training and testing. The learned policy is then directly transferred to our self-designed tracked robot in a real-world environment for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over hand-crafted control models and state-of-the-art DRL strategies for crossing complex terrains.

Due to the perilous conditions in coal mine accidents, trapped people need to be rescued as soon as possible. The danger and complexity of this environment have brought great difficulties for search and rescue. The emergence of rescue robots has addressed this problem, and the improvement of their search-and-rescue ability is a key concern for rescue robot designers. Based on previous experience of a tracked mobile robot, this paper proposes a new solution via the addition of adjustive rocker arms. For this purpose, a hydraulic unit is installed between the rocker arms and the robot, which enables the rocker arms to be pulled back and forth, enabling its adaptation to more complex environments. In addition, based on kinematics equations, by analyzing the motion rules and characteristics of climbing typical obstacles including stairs, grooves, and lug steps, the present research not only works out the centroid coordinates but also explores the maximal obstacle height. The results show that when the rocker arms are extended by hydraulic pressure, the obstacle-climbing height of the robot increases 16.7 percent. This demonstrates the improvement in obstacle climbing ability and rescue efficiency in complex environments. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the perilous conditions in coal mine accidents, trapped people need to be rescued as soon as possible. The danger and complexity of this environment have brought great difficulties for search and rescue. The emergence of rescue robots has addressed this problem, and the improvement of their search-and-rescue ability is a key concern for rescue robot designers. Based on previous experience of a tracked mobile robot, this paper proposes a new solution via the addition of adjustive rocker arms. For this purpose, a hydraulic unit is installed between the rocker arms and the robot, which enables the rocker arms to be pulled back and forth, enabling its adaptation to more complex environments. In addition, based on kinematics equations, by analyzing the motion rules and characteristics of climbing typical obstacles including stairs, grooves, and lug steps, the present research not only works out the centroid coordinates but also explores the maximal obstacle height. The results show that when the rocker arms are extended by hydraulic pressure, the obstacle-climbing height of the robot increases 16.7 percent. This demonstrates the improvement in obstacle climbing ability and rescue efficiency in complex environments.

This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques.

For individuals trapped in a fire, early detection can significantly increase their chances of survival. Presently, firefighters employ a systematic search method along the walls, ensuring a comprehensive and thorough exploration of the environment to prevent missing any trapped victims. However, this approach has its drawbacks, such as time wastage in areas or rooms without victims. The goal of this study is to propose a search method that can expedite the discovery of trapped victims. Our method relies primarily on prior knowledge, crowd information and the temperature within the fire scene, utilizing fuzzy logic to calculate the probability of the presence and survival of victims in each distinct space. The resulting search sequence not only accelerates victim discovery but also safeguards firefighters from unnecessary risks. We conducted various simulation experiments to validate our approach. In comparison to the sequential search method currently employed by firefighters, the experimental results indicate that our method reduces the time to locate trapped victims by 25 %. Compared to other path planning algorithms based on approximate optimization, our method offers a shorter computation time, making it suitable for integration into fire rescue systems to provide real-time assistance. [ABSTRACT FROM AUTHOR]