# A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case

arXiv: https://arxiv.org/abs/2501.02982 · HTML: https://mlsanity.com/paper/2501.02982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 135 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors](https://mlsanity.com/paper/1910.07960/) (2019)
- [Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets](https://mlsanity.com/paper/2104.13018/) (2021)
- [Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review](https://mlsanity.com/paper/1904.02048/) (2019)
- [A Bio-inspired Collision Detecotr for Small Quadcopter](https://mlsanity.com/paper/1801.04530/) (2018)
- [An LGMD Based Competitive Collision Avoidance Strategy for UAV](https://mlsanity.com/paper/1904.07206/) (2019)
- [A neural model of the locust visual system for detection of object approaches with real-world scenes](https://mlsanity.com/paper/1801.08108/) (2018)
- [Complementary Visual Neuronal Systems Model for Collision Sensing](https://mlsanity.com/paper/2006.06431/) (2020)
- [OppLoD: the Opponency based Looming Detector, Model Extension of Looming Sensitivity from LGMD to LPLC2](https://mlsanity.com/paper/2302.10284/) (2023)

## Built on by (most cited first, 1 of 1)

- [Probing Geospatial SSL Representations with Environmental Signals](https://mlsanity.com/paper/2607.05207/) (2026)

## Abstract (arXiv)

Compared to human vision, locust visual systems excel at rapid and precise collision detection, despite relying on only hundreds of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modeling of LGMD's visual neural circuits, and LGMD-based robotics have advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modeling, and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modeling, and robotics. Furthermore, this paradigm would shed light upon the modeling and robotic research into other motion-sensitive neurons or neural circuits.