ImPACT-TRC Thin Serpentine Robot Platform for Urban Search and Rescue

Konyo, Masashi; Ambe, Yuichi; Nagano, Hikaru; Yamauchi, Yoshiko; Tadokoro, Satoshı; Bando, Yoshiaki; Itoyama, Katsutoshi; Okuno, Hiroshi G.; Okatani, Takayuki; Shimizu, Kanta; Ito, Eisuke

doi:10.1007/978-3-030-05321-5_2

Cited by 15 publications

(13 citation statements)

References 57 publications

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“…Two complimentary examples of ground robots for SAR operations are introduced in [41], where both large and small robots are described. Ground robots for SAR missions can be characterized among those with dexterous manipulation capabilities and robust mobility on uneven terrain, such as the robot developed within the CENTAURO project [14], smaller robots with the ability of moving through tight spaces [41], or serpentine-like robots able of tethered operation across complex environments [42]. The recent DARPA SubT challenge has seen the design and deployment of flexible and robust ground units able of long-term autonomy and carry aerial units.…”

Section: ) Ground Robotsmentioning

confidence: 99%

“…Other robots have taken inspiration from video scopes and fiber scopes utilized to obtain imagery from confined spaces [69], [70]. In [42], researchers participating in the ImPACT-TRC challenge presented a thin serpentine robot platform, a long and flexible continuum robot with a length of up to 10 m and a diameter of just 50 mm, able to localize itself with visual SLAM and access collapsed buildings.…”

Section: Urban Sarmentioning

confidence: 99%

“…Content may change prior to final publication. [28], [42], [53]- [58], Maritime SAR [12], [22], [59]- [61], and Wilderness SAR [62]- [65]. (i) UAVs can provide a significant improvement at sea in term of situational awareness from the air, and can be deployed on-site even from small ships.…”

Section: Triagementioning

confidence: 99%

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Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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Section: ) Ground Robotsmentioning

confidence: 99%

Section: Urban Sarmentioning

confidence: 99%

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Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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“…Therefore, the postcatastrophe support and rescue are equally important as the disaster prediction and warning process. Autonomous and intelligent assistive systems have been prevalent in carrying on supportive missions in many hostile environments after disasters [2][3][4]. Non-human agents such as robots are increasingly exploited in such situations, especially in working in toxic and hazardous zones.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

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This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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“…Indeed, the effectiveness of robots is twofold: from one hand, they can support rescuers to improve the efficiency of search operations and, on the other hand, they can even substitute humans thus decreasing the risks associated to operations in harsh environments (fires, contaminated sites, ...). To this end, a variety of ground [1][2][3] and aerial [4,5] robotic platforms, characterized by different capabilities and equipment, have been developed to support S&R operations in different scenarios such as urban disasters, mining accidents, explosions in radiation sites, etc.…”

Section: Introductionmentioning

confidence: 99%

An ARVA Sensor Simulator

Cacace

Mimmo

Marconi

2020

Studies in Computational Intelligence

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Robot Operating System (ROS) framework supports different kind of sensors, typically used to develop robotic applications, such as vision and depth sensors, laser scanners and so on. In this chapter, we present a new ROS package designed to simulate ARVA transceiver sensors: arva_sim. ARVA is a French acronym which stands for Appareil de Recherche de Victims en Avalanche and represents the forefront technology adopted in Search & Rescue operations to localize victims of avalanches buried under the snow. In order to simulate its behavior, this package provides two Gazebo plugins: the transmitter and receiver. The aim of this chapter is to describe the mathematical and theoretical background of the transceiver, discussing its implementation and integration with ROS. To demonstrate the accuracy of the proposed sensor model, we present a simulation scenario in which an Unmanned Aerial Vehicle (UAV) equipped with the transceiver sensor performs a basic S&R pattern using the output of ARVA system. It is worth nothing that the proposed ROS package, arva_sim, represents the first simulation model of an ARVA transceiver system and can be useful for the developer to design, test and benchmark faster and smarter search strategies to speed up rescue missions in case of avalanches.

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ImPACT-TRC Thin Serpentine Robot Platform for Urban Search and Rescue

Cited by 15 publications

References 57 publications

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

An ARVA Sensor Simulator

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