The maintenance of pipelines is constrained by their inaccessibility. An EU-funded project developed swarms of small autonomous remote-sensing agents that learn through experience to explore and map such networks. The technology could be adapted to a wide range of hard-to-access artificial and natural environments.
© Bart van Overbeeke, 2019
There is a lack of technology for exploring inaccessible environments, such as water distribution and other pipeline networks. Mapping these networks using remote-sensing technology could locate obstructions, leaks or faults to deliver clean water or prevent contamination more efficiently. The long-term challenge is to optimise remote-sensing agents in a way that is applicable to many inaccessible artificial and natural environments.
The EU-funded PHOENIX project addressed this with a method that combines innovations in hardware, sensing and artificial evolution, using small spherical remote sensors called motes.
We integrated algorithms into a complete co-evolutionary framework where motes and environment models jointly evolve, say project coordinator Peter Baltus of Eindhoven University of Technology in the Netherlands. This may serve as a new tool for evolving the behaviour of any agent, from robots to wireless sensors, to address different needs from industry.
The teams method was successfully demonstrated using a pipeline inspection test case. Motes were injected multiple times into the test pipeline. Moving with the flow, they explored and mapped its parameters before being recovered.
Motes operate without direct human control. Each one is a miniaturised smart sensing agent, packed with microsensors and programmed to learn by experience, make autonomous decisions and improve itself for the task at hand. Collectively, motes behave as a swarm, communicating via ultrasound to build a virtual model of the environment they pass through.
The key to optimising the mapping of unknown environments is software that enables motes to evolve self-adaptation to their environment over time. To achieve this, the project team developed novel algorithms. These bring together different kinds of expert knowledge, to influence the design of motes, their ongoing adaptation and the rebirth of the overall PHOENIX system.
Artificial evolution is achieved by injecting successive swarms of motes into an inaccessible environment. For each generation, data from recovered motes is combined with evolutionary algorithms. This progressively optimises the virtual model of the unknown environment as well as the hardware and behavioural parameters of the motes themselves.
As a result, the project has also shed light on broader issues, such as the emergent properties of self-organisation and the division of labour in autonomous systems.
To control the PHOENIX system, the project team developed a dedicated human interface, where an operator initiates the mapping and exploration activities. State-of-the-art research is continuing to refine this, along with minimising microsensor energy consumption, maximising data compression and reducing mote size.
The projects versatile technology has numerous potential applications in difficult-to-access or hazardous environments. Motes could be designed to travel through oil or chemical pipelines, for example, or discover sites for underground carbon dioxide storage. They could assess wastewater under damaged nuclear reactors, be placed inside volcanoes or glaciers, or even be miniaturised enough to travel inside our bodies to detect disease.
Thus, there are many commercial possibilities for the new technology. In the Horizon 2020 Launchpad project SMARBLE, the business case for the PHOENIX project results is being further explored, says Baltus.