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Content archived on 2024-05-29

Group of unmanned assistant robots deployed in aggregative navigation supported by scent detection


The GUARDIANS are a swarm of autonomous robots applied to navigate and search an urban ground. The project's central example is an industrial warehouse in smoke, as proposed by the Fire and Rescue Service. The job is time consuming and dangerous; toxics may be released and humans senses can be severely impaired. They get disoriented and may get lost. The robots warn for toxic chemicals, provide and maintain mobile communication links, infer localisation information and assist in searching. They enhance operational safety and speed and thus indirectly save lives.

The robots navigate autonomously and accompany a human squad-leader. They connect to a wireless ad-hoc network and forward data to the squad-leader and the control station. The network is self-organising, adapts to connection failures by modifying its connections from local up to central connections. The autonomous swarm operates in communicative and non-communicative mode. In communicative mode automatic service discovery is applied: the robots find peers to help them. The wireless network also enables the robots to support a human squad-leader operating within close range. The aim is for flexible and seamless switching between these modes in order to compensate for loss of network signals and to support and safeguard the squad-leader. Several robot platforms are used, off-the-shelf mini-robots as well as middle sized robots. The emphasis in data collection is on toxic plume detection, to enable olfactory-based navigation, allow safe progress for the human squad-leader and to detect plume sources.

The major aim of the project is to develop a swarm of autonomous robots that is able to adequately assist and safeguard a human squad leader. The project organises workshops with end-users (rescue workers and fire-fighters) and the advisory board, to assess the demonstrations and to disseminate research results. The workshops, moreover, aim at exploring additional exploitation of results.

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