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M. Yedutenko
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2 records found
1
Journal article
(2025)
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S.T. Hazelaar, C. Wang, C. de Wagter, Florian T. Muijres, G.C.H.E. de Croon, M. Yedutenko
Since every flight ends in a landing and every landing is a potential crash, deceleration during landing is one of the most critical flying maneuvers. Here we implement a recently-discovered insect visual-guided landing strategy in which the divergence of optical flow is regulated in a step-wise fashion onboard a quadrotor for the task of visual servoing. This approach was shown to be a powerful tool for understanding challenges encountered by visually-guided flying systems. We found that landing on a relatively small target requires mitigation of the noise with adaptive low-pass filtering, while compensation for the delays introduced by this filter requires open-loop forward accelerations to switch from divergence setpoint. Both implemented solutions are consistent with insect physiological properties. Our study evaluates the challenges of visual-based navigation for flying insects. It highlights the benefits and feasibility of the switching divergence strategy that allows for faster and safer landings in the context of robotics.
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Since every flight ends in a landing and every landing is a potential crash, deceleration during landing is one of the most critical flying maneuvers. Here we implement a recently-discovered insect visual-guided landing strategy in which the divergence of optical flow is regulated in a step-wise fashion onboard a quadrotor for the task of visual servoing. This approach was shown to be a powerful tool for understanding challenges encountered by visually-guided flying systems. We found that landing on a relatively small target requires mitigation of the noise with adaptive low-pass filtering, while compensation for the delays introduced by this filter requires open-loop forward accelerations to switch from divergence setpoint. Both implemented solutions are consistent with insect physiological properties. Our study evaluates the challenges of visual-based navigation for flying insects. It highlights the benefits and feasibility of the switching divergence strategy that allows for faster and safer landings in the context of robotics.
TDE-3
An improved prior for optical flow computation in spiking neural networks
Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. In the present work, we propose an augmented 3-point TDE (TDE-3) with additional inhibitory input that makes TDE-3 direction-selectivity robust in textured environments. We developed a procedure to train the new TDE-3 using backpropagation through time and surrogate gradients to linearly map input velocities into an output spike count or an Inter-Spike Interval (ISI). Using synthetic data, we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise. ISI turns out to be more robust toward variation in spatial frequency, whereas the spike count is a more reliable training signal in the presence of noise. We conducted an in-depth quantitative investigation of optical flow coding with TDE and compared TDE-2 vs. TDE-3 in terms of energy efficiency and coding precision. The results show that at the network level, both detectors show similar precision (20° angular error, 88% correlation with the truth of the ground). However, due to the more robust direction selectivity of individual TDEs, the TDE-3 based network spikes less and is hence more energy efficient. Reported precision is on par with model-based methods but the spike-based processing of the TDEs provides allows more energy-efficient inference with neuromorphic hardware. Additionally, we also employed TDE-2 and TDE-3 to estimate ego-motion and showed results competitive with those achieved by neural networks with 1.5 × 105 parameters.
...
Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. In the present work, we propose an augmented 3-point TDE (TDE-3) with additional inhibitory input that makes TDE-3 direction-selectivity robust in textured environments. We developed a procedure to train the new TDE-3 using backpropagation through time and surrogate gradients to linearly map input velocities into an output spike count or an Inter-Spike Interval (ISI). Using synthetic data, we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise. ISI turns out to be more robust toward variation in spatial frequency, whereas the spike count is a more reliable training signal in the presence of noise. We conducted an in-depth quantitative investigation of optical flow coding with TDE and compared TDE-2 vs. TDE-3 in terms of energy efficiency and coding precision. The results show that at the network level, both detectors show similar precision (20° angular error, 88% correlation with the truth of the ground). However, due to the more robust direction selectivity of individual TDEs, the TDE-3 based network spikes less and is hence more energy efficient. Reported precision is on par with model-based methods but the spike-based processing of the TDEs provides allows more energy-efficient inference with neuromorphic hardware. Additionally, we also employed TDE-2 and TDE-3 to estimate ego-motion and showed results competitive with those achieved by neural networks with 1.5 × 105 parameters.