Abstract

The developments of robotics inform research across a broad range of disciplines. In this paper, we will study and compare two collision selective neuron models via a vision-based autonomous micro robot. In the locusts' visual brain, two Lobula Giant Movement Detectors (LGMDs), i.e. LGMD1 and LGMD2, have been identified as looming sensitive neurons responding to rapidly expanding objects, yet with different collision selectivity. Both neurons have been built for perceiving potential collisions in an efficient and reliable manner; a few modeling works have also demonstrated their effectiveness for robotic implementations. In this research, for the first time, we set up binocular neuronal models, combining the functionalities of LGMD1 and LGMD2 neurons, in the visual modality of a ground mobile robot. The results of systematic on-line experiments demonstrated three contributions: (1) The arena tests involving multiple robots verified the robustness and efficiency of a reactive motion control strategy via integrating a bilateral pair of LGMD1 and LGMD2 models for collision detection in dynamic scenarios. (2) We pinpointed the different collision selectivity between LGMD1 and LGMD2 neuron models fulfilling corresponded biological research results. (3) The low-cost robot may also shed lights on similar bio-inspired embedded vision systems and swarm robotics applications.