Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study

Zhang, Di; Sessa, Salvatore; Kasai, R.; Cosentino, Sarah; Giacomo, Cimarelli; Mochida, Yasuaki; Yamada, Hiroya; Guarnieri, Michele; Takanishi, Atsuo

doi:10.3390/s18030852

Cited by 24 publications

(10 citation statements)

References 25 publications

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“…This can be achieved by enhancing the sufficiency and quality of the thermal information provided to the algorithm using high‐resolution IR cameras. [ 57,58 ] In addition, the algorithm's practicality can be strengthened by training it with more diverse datasets, such as those containing human images captured at different angles of view, which are likely to occur as the hybrid robots dynamically change their orientations when traversing complex terrains. Additionally, the backpack can be equipped with other sensors to enrich the collected information (e.g., CO 2 sensors [ 58 ] ), thereby increasing reliability in survivor detection.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Insect–Computer Hybrid Robot: Installing Innate Obstacle Negotiation and Onboard Human Detection onto Cyborg Insect

Tran-Ngoc

Chong

et al. 2023

Advanced Intelligent Systems

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Developing small mobile robots for Urban Search and Rescue (USAR) is a major challenge due to constraints in size and power required to perform vital functions such as obstacle navigation, victim detection, and wireless communication. Drawing upon the idea that insects’ locomotion can be controlled, what if we further utilize the insects’ intrinsic ability to avoid obstacles? Herein, a cockroach hybrid robot (≈ 1.5 cm height, 5.7 cm length) that implements the abovementioned functions is developed. It is tested in an arena with randomly placed obstacles, and a motion capture system is used to track the insect's position among the untracked obstacles. A navigation algorithm that uses an inertial measurement unit (IMU) is developed to heuristically predict the insect's situation and stimulate the insect to escape nearby obstacles. The utilization of insect's intrinsic locomotor ability and low‐powered IMU reduces the onboard power load, allowing the addition of a human‐detecting function. An image classification model enables the use of an onboard low‐resolution infrared camera for human detection. Consequently, a single hybrid robot is established that includes locomotion control, autonomous navigation in obstructed areas, onboard human detection, and wireless communication, representing a significant step toward real USAR application.

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Section: Discussionmentioning

confidence: 99%

Intelligent Insect–Computer Hybrid Robot: Installing Innate Obstacle Negotiation and Onboard Human Detection onto Cyborg Insect

Tran-Ngoc

Chong

et al. 2023

Advanced Intelligent Systems

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“…We explicitly avoid handling the collision problem (which is not the focus of this document and for which there is a large literature [20]). Connected to this, we assume that the UAVs are not equipped with infrared cameras, in fact, thermal imaging cameras have proven to be reliable in detecting humans over short distances (hence low and fixed altitude is required, making it more difficult to avoid collisions), but it results in an ambiguous blob formation when used from long distances or in the presence of numerous obstacles [21].…”

Section: A Real Life Situationmentioning

confidence: 99%

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

2022

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In this paper, we consider the problem of completely flying over an area just hit by an earthquake with a fleet of Unmanned Aerial Vehicles (UAVs) to opportunely direct rescue teams. The cooperation between UAVs ensures that the search for possible survivors can be faster and more effective than the solutions currently implemented by civil protection.To study this scenario, we introduce the Cover by Multitrips with Priorities (CMP) problem, which tries to keep into account all the main real-life issues connected to the flight and coordination of the UAVs. We conduct a theoretical study to estimate the best number of UAVs and additional batteries, to give indications to the organization that leads the rescue teams to be able to guarantee rapid and effective rescue. Finally, based on some theoretical considerations, we propose some heuristics that tackle the problem of flying over the whole area with a fleet of UAVs in the shortest possible time. Simulations show that they work efficiently in both the proposed scenarios and provide better performance than previous solutions once they are arranged to work in our scenarios. The main advantages of our approach w.r.t. the current drone-based solutions used by the civil defense are that UAVs do not need drivers so the time of all available rescue workers can be invested in doing something else. In our model, we take into account that some sites (e.g. buildings with a high fire risk or schools and hospitals) have a higher priority and must be inspected first, and the possibility that UAVs can make a decision based on what they detect. Finally, our approach allows UAVs to collaborate so that the same sites will be flown over exactly once in order to speed up the rescue mission.

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“…Most of the earthquakes above 5.5 on the Richter scale can cause large-scale destruction through building collapse and structural damage [2]. Approximately 80 percent of victims can be successfully rescued alive if they are detected by help teams within 48 h [3]. This means that detecting an injured victim and providing medical care in the shortest time is a priority of any disaster rescue operation.…”

Section: Introductionmentioning

confidence: 99%

Finding Earthquake Victims by Voice Detection Techniques

Jha

Lang

Jedermann

2021

The 8th International Electronic Conference on Sensors and Applications

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After an earthquake or a building collapse, victim recovery is a challenging task. Recovery methods must include the location of victims by non-visual means; human speech is one such parameter that can be used in victim and rescue operations. In this paper, we discuss the application of a voice-detection technique for the discrimination of voice and non-voice sounds, based on frequency parameters such as flux, centroid, and roll-off of audio signals. Using the cross-validation tests based on a linear discriminant analysis model, flux and centroid individually displayed the highest success rates for all categories of test sample. By combining these two parameters, the recognition rate was improved to 78% for the signals with high background noise.

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Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study

Cited by 24 publications

References 25 publications

Intelligent Insect–Computer Hybrid Robot: Installing Innate Obstacle Negotiation and Onboard Human Detection onto Cyborg Insect

Intelligent Insect–Computer Hybrid Robot: Installing Innate Obstacle Negotiation and Onboard Human Detection onto Cyborg Insect

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

Finding Earthquake Victims by Voice Detection Techniques

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