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.
To realize the neural characteristics of LGMDs, a few computational models have been proposed for LGMD1 @cite_4 @cite_10 @cite_41 @cite_38 @cite_20 and successfully utilized in vision-based platforms such as vehicles @cite_8 @cite_14 and robots @cite_3 @cite_40 @cite_25 @cite_2 @cite_11 @cite_31 for collision detection. Nevertheless, very little modeling works have been conducted for LGMD2. In this article, we will propose a visual neural network to fulfill the specific properties of LGMD2 and explore its potential with systematic experiments. The preliminary results of this research has been partially published in @cite_34 @cite_19 .
@cite_38 @cite_14 @cite_4 @cite_8 @cite_41 @cite_3 @cite_19 @cite_40 @cite_2 @cite_31 @cite_34 @cite_10 @cite_25 @cite_20 @cite_11
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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. Two Lobula Giant Movement Detectors (LGMDs) have been identified in the lobula region of the locust visual system: LGMD1 and LGMD2. LGMD1 had been successfully used in robot navigation to avoid impending collision. LGMD2 also responds to looming stimuli in depth, and shares most the same properties with LGMD1; however, LGMD2 has its specific collision selective responds when dealing with different visual stimulus. Therefore, in this paper, we propose a novel way to model LGMD2, in order to emulate its predicted bio-functions, moreover, to solve some defects of previous LGMD1 computational models. The mechanism of ON and OFF cells, as well as bio-inspired nonlinear functions, are introduced in our model, to achieve LGMD2's collision selectivity. Our model has been tested by a miniature mobile robot in real time. The results suggested this model has an ideal performance in both software and hardware for collision recognition. The lobula giant movement detector (LGMD) neuron of locusts has been shown to preferentially respond to objects approaching the eye of a locust on a direct collision course. Computer simulations of the neuron have been developed and have demonstrated the ability of mobile robots, interfaced with a simulated LGMD model, to avoid collisions. In this study, a model of the LGMD neuron is presented and the functional parameters of the model identified. Models with different parameters were presented with a range of automotive video sequences, including collisions with cars. The parameters were optimised to respond correctly to the video sequences using a range of genetic algorithms (GAs). The model evolved most rapidly using GAs with high clone rates into a form suitable for detecting collisions with cars and not producing false collision alerts to most non-collision scenes. 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.