2 research outputs found
AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing
Rescue vessels are the main actors in maritime safety and rescue operations.
At the same time, aerial drones bring a significant advantage into this
scenario. This paper presents the research directions of the AutoSOS project,
where we work in the development of an autonomous multi-robot search and rescue
assistance platform capable of sensor fusion and object detection in embedded
devices using novel lightweight AI models. The platform is meant to perform
reconnaissance missions for initial assessment of the environment using novel
adaptive deep learning algorithms that efficiently use the available sensors
and computational resources on drones and rescue vessel. When drones find
potential objects, they will send their sensor data to the vessel to verity the
findings with increased accuracy. The actual rescue and treatment operation are
left as the responsibility of the rescue personnel. The drones will
autonomously reconfigure their spatial distribution to enable multi-hop
communication, when a direct connection between a drone transmitting
information and the vessel is unavailable
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Autonomous or teleoperated robots have been playing increasingly important
roles in civil applications in recent years. Across the different civil domains
where robots can support human operators, one of the areas where they can have
more impact is in search and rescue (SAR) operations. In particular,
multi-robot systems have the potential to significantly improve the efficiency
of SAR personnel with faster search of victims, initial assessment and mapping
of the environment, real-time monitoring and surveillance of SAR operations, or
establishing emergency communication networks, among other possibilities. SAR
operations encompass a wide variety of environments and situations, and
therefore heterogeneous and collaborative multi-robot systems can provide the
most advantages. In this paper, we review and analyze the existing approaches
to multi-robot SAR support, from an algorithmic perspective and putting an
emphasis on the methods enabling collaboration among the robots as well as
advanced perception through machine vision and multi-agent active perception.
Furthermore, we put these algorithms 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). This is, to the best of our
knowledge, the first review considering heterogeneous SAR robots across
different environments, while giving two complimentary points of view: control
mechanisms and machine perception. Based on our review of the state-of-the-art,
we discuss the main open research questions, and outline our insights on the
current approaches that have potential to improve the real-world performance of
multi-robot SAR systems