Robotic Urban Search and Rescue: A Survey from the Control Perspective
By Yugang Liu, Goldie Nejat. J. Intell. Robotic Syst., 2013
294 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/6014d028e697ba24682af20532f5f081704b63cd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Jorge Peña Queralta
- Alejandro Ribeiro
- Fernando Gama
- Tomi Westerlund
- Amanda Prorok
- Qingbiao Li
- Kaicheng Zhang
- Goldie Nejat
In Computer Vision and Pattern Recognition
- Jorge Peña Queralta
- Alejandro Ribeiro
- Hannu Tenhunen
- Fernando Gama
- Tomi Westerlund
- Bilge Can Pullinen
- Amanda Prorok
- Qingbiao Li
Citations in the corpus
- Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments (2019, background): “The advantage of using such robots in USAR is to help reduce the workload of rescue workers and improve their situational awareness [1].”
- Graph Neural Networks for Decentralized Multi-Robot Path Planning (2019, background): “nt Path Finding (MAPF), aims at generating collision-free paths leading robots from their origins to designated destinations. Solutions to this problem lend themselves to search and rescue operations [15], product pickup and delivery [12], item retrieval in warehouses [6], and mobility-on-demand ser”
- Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “Multiple works have been devoted to reviewing the different path planning strategies for unmanned vehicles in different mediums: aerial robots [133], surface robots [148], underwater robots [149], [150], and ground robots for urban [55], or wilderness [151] environments.”