  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 giant movement detectors (LGMDs) in locusts and the lobula plate
tangential cells (LPTCs) in flies, have been studied, intensively. The LGMDs
have specific selectivity to approaching objects in depth that threaten
collision; whilst the LPTCs are only sensitive to translating objects in
horizontal and vertical directions. Though each has been modelled and applied
in various visual scenes including robot scenarios, little has been done on
investigating their complementary functionality and selectivity when
functioning together. To fill this vacancy, we introduce a hybrid model
combining two LGMDs (LGMD-1 and LGMD-2) with horizontally (rightward and
leftward) sensitive LPTCs (LPTC-R and LPTC-L) specialising in fast collision
perception. With coordination and competition between different activated
neurons, the proximity feature by frontal approaching stimuli can be largely
sharpened up by suppressing translating and receding motions. The proposed
method has been implemented in ground micro-mobile robots as embedded systems.
The multi-robot experiments have demonstrated the effectiveness and robustness
of the proposed model for frontal collision sensing, which outperforms previous
single-type neuron computation methods against translating interference.
