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/Job opportunities/Neuromorphic multi-drone perception

Neuromorphic multi-drone perception

PhD - Antwerpen | More than two weeks ago

United they fly! Help us build a distributed drone brain.

The trend towards autonomous drones is currently driving the integration of an increasing number of sensors for safe navigation under all circumstances, forcing algorithms and hardware to be energy efficient and fast. When drone technology continues to mature, deploying swarms of them will enable even more advanced use cases, for example in precision agriculture. Swarms also offer the possibility of sharing both sensory and compute resources, making the swarm act and respond as a single collaborative entity with overall better performance.

​In this PhD project, you will work with real-world multi-sensory data collected by multiple drones and develop a spike-based neuromorphic fusion solution running on custom imec hardware. More specifically, you will focus on the following research questions:

  • Can we build a low power sensor fusion solution based on spiking neural networks for autonomous drone navigation and obstacle avoidance, running on imec hardware. We will investigate different solutions to perform spike encodings and carry out the learning. A trade off will be made vs power consumption and hardware.
  • How can collaborative drones, each with their own spike-based neuromorphic fusion solution, communicate with each other in a timely and resource efficient way? Which sensor fusion tasks need to be performed by which nodes in a collaborative setting?
  • Can we develop efficient techniques for distributed training across multiple spike-based drones to reduce each drone's individual memory and power requirement and, at the same time, lower the convergence time of the swarm?

Required background: Engineering science, computer/data science

Type of work: 60% modeling/simulation, 30% experimental, 10% literature

Supervisor: Steven Latré

Co-supervisor: Werner Van Leekwijck

Daily advisor: Ilja Ocket

The reference code for this position is 2021-119. Mention this reference code on your application form.

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