Dr Michael Mangan
MEng, MSc, PhD
Department of Computer Science
Lecturer in Machine Learning and Robotics
Operations Director of Sheffield Robotics
Member of the Machine Learning research group
+44 114 222 1905
Full contact details
Department of Computer Science
Regent Court (DCS)
Michael Mangan joined the Department of Computer Science at the University of Sheffield in April 2018. Before joining he was a Senior Lecturer in Computer Science in College of Science and member of the Lincoln Centre for Autonomous Systems (L-CAS).
He received his undergraduate degree in Avionics (MEng) from the University of Glasgow in 2004, before moving to the University of Edinburgh where he completed an MSc in Neuroinformatics (2006) and a PhD in biorobotics (2011).
He remained at the University of Edinburgh for the next 4 years receiving funding awards from the BBSRC and EPSRC. He joined the Lincoln Centre for Autonomous Systems in April, 2016.
- Research interests
Dr Mangan's primary research focuses on modelling the navigational behaviour of insects, which are able to travel through complex environments robustly despite their limited nervous and sensory systems. Revealing the sensory and algorithmic underpinning of these capabilities will not only aid the understanding of biological systems but may also offer engineering solutions - building robots able to navigate as well as the humble ant.
- A decentralised neural model explaining optimal integration of navigational strategies in insects. eLife, 9.
- Route-following ants respond to alterations of the view sequence. The Journal of Experimental Biology.
- Multimodal interactions in insect navigation. Animal Cognition. View this article in WRRO
- Towards image-based animal tracking in natural environments using a freely moving camera. Journal of Neuroscience Methods, 330. View this article in WRRO
- L*a*b*fruits : a rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks. Sensors, 20(1). View this article in WRRO
- From skylight input to behavioural output : a computational model of the insect polarised light compass. PLOS Computational Biology, 15(7). View this article in WRRO
- Rotation invariant visual processing for spatial memory in insects. Interface Focus, 8(4), 20180010-20180010. View this article in WRRO
- Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes. Environmental Modelling & Software, 99, 88-100.
- How Ants Use Vision When Homing Backward. Current Biology, 27(3), 401-407. View this article in WRRO
- Ant homing ability is not diminished when traveling backwards. Frontiers in Behavioral Neuroscience, 10(1).
- Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments. PLoS Computational Biology, 12(2), e1004683-e1004683.
- Optimal cue integration in ants. Proceedings of the Royal Society B: Biological Sciences, 282(1816).
- How variation in head pitch could affect image matching algorithms for ant navigation. Journal of Comparative Physiology A, 201(6), 585-597. View this article in WRRO
- Insect navigation: do ants live in the now?. Journal of Experimental Biology, 218(6), 819-823.
- Still no convincing evidence for cognitive map use by honeybees. Proceedings of the National Academy of Sciences, 111(42), E4396-E4397.
- Snapshots in ants? New interpretations of paradigmatic experiments. Journal of Experimental Biology, 216(10), 1766-1770.
- Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox). Behavioral Ecology, 23(5), 944-954.
- Modelling place memory in crickets. Biological Cybernetics, 101(4), 307-323.
- Regarding Compass Response Functions For Modeling Path Integration: Comment on “Evolving a Neural Model of Insect Path Integration”. Adaptive Behavior, 16(4), 275-276.
- Path Integration Using a Model of e-Vector Orientation Coding in the Insect Brain: Reply to Vickerstaff and Di Paolo. Adaptive Behavior, 16(4), 277-280.
- Place memory in crickets. Proceedings of the Royal Society B: Biological Sciences, 275(1637), 915-921.
- Evolving a Neural Model of Insect Path Integration. Adaptive Behavior, 15(3), 273-287.
- Biomimetic and Biohybrid Systems Springer International Publishing
- How Active Vision Facilitates Familiarity-Based Homing, Biomimetic and Biohybrid Systems (pp. 427-430). Springer Berlin Heidelberg
Conference proceedings papers
- An Analysis of a Ring Attractor Model for Cue Integration (pp 459-470) View this article in WRRO
- Visual tracking of small animals in cluttered natural environments using a freely moving camera. 2017 IEEE International Conference on Computer Vision Workshops (pp 2840-2849). Venice, Italy, 22 October 2017 - 29 October 2017. View this article in WRRO
- Using the Robot Operating System for Biomimetic Research. Conference on Biomimetic and Biohybrid Systems (pp 515-521)
- Route following without scanning. Conference on Biomimetic and Biohybrid Systems (pp 199-210)
- Sky segmentation with ultraviolet images can be used for navigation. Robotics: Science and Systems X, 12 July 2014 - 16 July 2014.
- How active vision facilitates familiarity-based homing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8064 LNAI (pp 427-430)
- Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations. UKRAS20 Conference: “Robots into the real world” Proceedings
- Towards Insect Inspired Visual Sensors for Robots. UKRAS20 Conference: “Robots into the real world” Proceedings
Current research grants
ActiveAI - active learning and selective attention for robust, transparent and efficient AI, EPSRC, 11/2019 - 10/2022, £953,584, as Co-PI
Brains on Board: Neuromorphic Control of Flying Robots, EPSRC, 12/2016 - 12/2021, £2,128,934, as Co-PI
Previous research grants
- Exploiting invisible cues for robot navigation in complex natural environments, EPSRC, 02/2015 - 08/2018, £558,417, as Researcher Co-PI
- Professional activities
Member of the Machine Learning research group and Sheffield Robotics