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 detection in complex environments. In the cockpit of locusts, two Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified which respond to looming objects rigorously with high firing rates. Compared to LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to dark moving objects against bright background in depth while not responding to light objects embedded in dark background - a similar situation which ground vehicles and robots are facing with. However, little work has been done on modeling LGMD2, let alone its potential in robotics and other vision-based applications. In this article, we propose a novel way of modeling LGMD2 neuron, with biased ON and OFF pathways splitting visual streams into parallel channels encoding brightness increments and decrements separately to fulfill its selectivity. Moreover, we apply a biophysical mechanism of spike frequency adaptation to shape the looming selectivity in such a collision-detecting neuron model. The proposed visual neural network has been tested with systematic experiments, challenged against synthetic and real physical stimuli, as well as image streams from the sensor of a miniature robot. The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms. The robotic experiments also showed its robustness in collision detection - it performed well for near range navigation in an arena with many obstacles. Its enhanced collision selectivity to dark approaching objects versus receding and translating ones has also been verified via systematic experiments.
In the LGMD1 state-of-the-art frameworks, e.g. @cite_4 @cite_20 @cite_25 , the visual signals are only processed in a single pathway. However, very recently, a LGMD1 modeling work @cite_31 with relevant mechanism of ON and OFF cells was demonstrated, which shed lights on that such a dual-channel structure could also be conducted for LGMD neurons. Moreover, when modeling practical collision-detecting vision systems, the selective response to objects approach rather than recession should be enhanced. Some methods have been proposed to help discriminate approach from recession, such as comparing the angular speeds sizes @cite_18 , or monitoring the membrane potential change gradient @cite_10 , etc. However, such avenues either require costly computational power or have no biological plausibility. On this aspect, the separated ON and OFF visual pathways could be the secret mechanism underlying unique collision selectivity, especially for LGMD2 neuron, i.e., its unique feature of only selectively responding to dark looming objects embedded in bright background can be fulfilled via such a biological structure.
@cite_18 @cite_4 @cite_31 @cite_10 @cite_25 @cite_20
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In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons. There are many ways of building collision-detecting systems. In this paper, we propose a novel collision selective visual neural network inspired by LGMD2 neurons in the juvenile locusts. Such collision-sensitive neuron matures early in the ﬁrst-aged or even hatching locusts, and is only selective to detect looming dark objects against bright background in depth, represents swooping predators, a situation which is similar to ground robots or vehicles. However, little has been done on modeling LGMD2, let alone its potential applications in robotics and other vision-based areas. Compared to other collision detectors, our major contributions are ﬁrst, enhancing the collision selectivity in a bio-inspired way, via constructing a computing efﬁcient visual sensor, and realizing the revealed speciﬁc characteristic sofLGMD2. Second, we applied the neural network to help rearrange path navigation of an autonomous ground miniature robot in an arena. We also examined its neural properties through systematic experiments challenged against image streams from a visual sensor of the micro-robot. Detecting colliding objects in complex dynamic scenes is a difficult task for conventional computer vision techniques. However, visual processing mechanisms in animals such as insects may provide very simple and effective solutions for detecting colliding objects in complex dynamic scenes. In this paper, we propose a robust collision detecting system, which consists of a lobula giant movement detector (LGMD) based neural network and a translating sensitive neural network (TSNN), to recognise objects on a direct collision course in complex dynamic scenes. The LGMD based neural network is specialized for recognizing looming objects that are on a direct collision course. The TSNN, which fuses the extracted visual motion cues from several whole field direction selective neural networks, is only sensitive to translating movements in the dynamic scenes. The looming cue and translating cue revealed by the two specialized visual motion detectors are fused in the present system via a decision making mechanism. In the system, the LGMD plays a key role in detecting imminent collision; the decision from TSNN becomes useful only when a collision alarm has been issued by the LGMD network. Using driving scenarios as an example, we showed that the bio-inspired system can reliably detect imminent colliding objects in complex driving scenes. Due to their relatively simple nervous system, insects are an excellent way through which we can investigate how visual information is acquired and processed in order to trigger specific behaviours, as flight stabilization, flying speed adaptation, collision avoidance responses, among others. From the behaviours previously mentioned, we are particularly interested in visually evoked collision avoidance responses. These behaviors are, by necessity, fast and robust, making them excellent systems to study the neural basis of behavior. On the other hand, artificial collision avoidance is a complex task, in which the algorithms used need to be fast to process the captured data and then perform real time decisions. Consequently, neurorobotic models may provide a foundation for the development of more effective and autonomous devices. In this paper, we will focus our attention in the Lobula Giant Movement Detector (LGMD), which is a visual neuron, located in the third layer of the locust optic lobe, that responds selectively to approaching objects, being responsible for trigger collision avoidance maneuvers in locusts. This selectivity of the LGMD neuron to approaching objects seems to result from the dynamics of the network pre-synaptic to this neuron. Tipically, this modelation is done by a conventional Difference of Gaussians (DoG) filter. In this paper, we propose the integration of a different model, an Inversed Difference of Gaussians (IDoG) filter, which preserves the different level of brightness in the captured image, enhancing the contrast at the edges. This change is expected to increase the performance of the LGMD model. Finally, a comparative analysis of both modelations, as well as its effect in the final response of the LGMD neuron, will be performed.