Related papers: Parameter Optimization and Learning in a Spiking N…
The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the locust that detects looming objects and triggers its escape responses. Understanding the neural principles and networks that lead to these fast and robust responses…
Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs). This is especially true for small or micro UAVs, due to their limited computational power. In nature, flying insects with compact and simple…
In the central nervous systems of animals like pigeons and locusts, neurons were identified which signal objects approaching the animal on a direct collision course. Unraveling the neural circuitry for collision avoidance, and identifying…
Accurate, timely and selective detection of moving obstacles is crucial for reliable collision avoidance in autonomous robots. The area- and energy-inefficiency of CMOS-based spiking neurons for obstacle detection can be addressed through…
Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion…
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming…
Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air…
Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without…
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…
The Dynamic Vision Sensor (DVS) has many attributes that allow it to be well suited to the task for UAV Detection. This paper is the first to look at exploiting the features of an Event Camera solely for Drone Detection while combining it…
Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional convolutional neural networks achieve strong detection accuracy but are computationally…
For autonomous robots in dynamic environments mixed with human, it is vital to detect impending collision quickly and robustly. The biological visual systems evolved over millions of years may provide us efficient solutions for collision…
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying…
Looming detection plays an important role in insect collision prevention systems. As a vital capability evolutionary survival, it has been extensively studied in neuroscience and is attracting increasing research interest in robotics due to…
Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways.…
Inspired by insects' visual brains, this paper presents original modelling of a complementary visual neuronal systems model for real-time and robust collision sensing. Two categories of wide-field motion sensitive neurons, i.e., the lobula…
Many species show avoidance reactions in response to looming object approaches. In locusts, the corresponding escape behavior correlates with the activity of the lobula giant movement detector (LGMD) neuron. During an object approach, its…
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in…
Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, yet a complex challenges for modern computers. In vertebrates, the detection of object motion is performed by specialised retinal…
Autonomous obstacle avoidance is of vital importance for an intelligent agent such as a mobile robot to navigate in its environment. Existing state-of-the-art methods train a spiking neural network (SNN) with deep reinforcement learning…