  Rapid, accurate and robust detection of looming objects in cluttered moving
backgrounds is a significant and challenging problem for robotic visual systems
to perform collision detection and avoidance tasks. Inspired by the neural
circuit of elementary motion vision in the mammalian retina, this paper
proposes a bioinspired approach-sensitive neural network (ASNN) that contains
three main contributions. Firstly, a direction-selective visual processing
module is built based on the spatiotemporal energy framework, which can
estimate motion direction accurately via only two mutually perpendicular
spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural
network is modeled as a push-pull structure formed by ON and OFF pathways,
which responds strongly to approaching motion while insensitivity to lateral
motion. Finally, a method of directionally selective inhibition is introduced,
which is able to suppress the translational backgrounds effectively. Extensive
synthetic and real robotic experiments show that the proposed model is able to
not only detect collision accurately and robustly in cluttered and dynamic
backgrounds but also extract more collision information like position and
direction, for guiding rapid decision making.
