Cooperation of multi robots for disaster rescue

Ren, Qilei; Man, Ka Lok; Lim, Eng Gee; Lee, Jin Kyung; Kim, Kyung Ki

doi:10.1109/isocc.2017.8368834

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…Portfolio approaches combine different solvers to get a globally better one, and their efficiency was already shown in the CP field [1,2,9]. [3] 115 [3] 115 [3] 115 [1] 121 118 130 [4] 130 [11] 129 [6] 130 [7] 134 134 6 133 [13] 133 [5] 133 [8] 133 [0] 154 154 150 [11] 153 [10] 153 [10] 150 [13]…”

Section: Methodsmentioning

confidence: 99%

“…Note that the MRD application has been widely addressed to tackle realworld problems related to situation awareness issues, such as cooperative sensing using air-ground teams [3], disaster response [13], and exploration-rescue systems in hostile environments [14]. The aim of this paper is to propose more generic solutions that can be applied to such industrial and academic applications.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Constraint (12) states that the presences of elementary moves must be consistent with the presences of the selected paths. Constraints (13) and (14) define the start and end times of the moves from and to the first and last fictitious observations respectively. Constraints (15) to (17) enforce a handover period between the successive intervals involved in a chosen navigation path.…”

Section: Fine-grain Scheduling Model: Layer L2mentioning

confidence: 99%

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Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

Pacheco¹,

Pralet²,

Roussel³

2019

Lecture Notes in Computer Science

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In this paper, we consider a multi-robot deployment problem involving a set of robots which must realize observation tasks at different locations and navigate through a shared network of waypoints. To solve this problem, we develop a two-level approach which alternates between (a) quickly obtaining high-level schedules based on a coarse grain CP model which approximates navigation tasks as setup times between observations, and (b) generating more accurate schedules based on a fine grain CP model which takes into account all resource usage conflicts during traversals of the shared network. The low-level layer also contains an explanation module able to generate constraints holding on high-level decision variables. These constraints (or cuts) account for interferences found in the low-level solutions and which the high-level scheduler should take into account to minimize the makespan. The proposed variants of the cut generation strategy are incomplete, the aim being to obtain good quality solutions in a short time, and they differ in the way they allow to diversify search. Experiments show the efficiency of this approach and the complementarity of the cut generation schemes proposed.

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Section: Methodsmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Fine-grain Scheduling Model: Layer L2mentioning

confidence: 99%

See 1 more Smart Citation

Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

Pacheco¹,

Pralet²,

Roussel³

2019

Lecture Notes in Computer Science

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“…However, existing systems that provide position/location information are not a practical solution in dynamic disaster scenarios, since these systems mostly rely on pre-installed and calibrated environment infrastructures and they are designed to operate with maps [13]. Also, most of the existing approaches are subject to certain limitations for real-time applications, mainly due to additional constraints such as sensor limitations, dynamic obstacle and inter-robot collision avoidance in unknown environments [7][8][9][10]. Also, considering the worst-case disaster scenario, there may not have any communication networks available.…”

Section: Robot Platoonsmentioning

confidence: 99%

“…ICARUS [5,6] was a European project which developed integrated components to assist SAR robotics teams dealing with rescue missions in difficult and life threatening situations, to save the life of human survivors. Ren et al [7] demonstrated a rescue operation in a real disaster scenario, where they used UGVs to clear rocks from the robot's path. For combining multiple SAR robots into a team, communication between robots is crucial.…”

Section: Introductionmentioning

confidence: 99%

Robot Platooning Strategy for Search and Rescue Operations

Manuel

Faied

Krishnan

et al. 2021

Intel Serv Robotics

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Major abrupt-onset cataclysmic events such as earthquakes, storms, floods, etc., typically damage infrastructure, cause injury, trap numerous individuals, and result in a massive death toll. A prompt life-saving response is required to rescue those who are marooned or trapped under debris. The difference between life and death can be a matter of how fast search and rescue attempts are carried out. On the other hand, these life-saving search and rescue operations are faced with real-time dynamic changes in the disaster site, in addition to possible communication network failure. This paper proposes a novel vision-based robot platooning algorithm that is capable of maneuvering teams of search and rescue robots in a dynamic disaster site, under the worst-case scenario of no available communication network. The algorithm was tested to drive teams of Pioneer-P3Dx and Jackal robots in five real different challenging disaster sites. The proposed algorithm showed enough robustness in all experiments to adapt to the dynamic environmental changes and drove the platoon to the desired destinations even when the team leader was lost.

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