Neural Based Obstacle Avoidance with CPG Controlled Hexapod Walking Robot

In this work, we are proposing a collision avoidance system for a hexapod crawling robot based on the detection of intercepting objects using the Lobula giant movement detector (LGMD) connected directly to the locomotion control unit based on the Central pattern generator (CPG). We have designed and experimentally verified the proposed approach that maps the output of the LGMD directly on the locomotion control parameters of the CPG. The results of the experimental verification of the system with real mobile hexapod crawling robot support the feasibility of the proposed approach in collision avoidance scenarios.