Gao, Jiyong, and Chaomin Luo
Safe or collision-free navigation of an autonomous mobile robot is one of the major challenges in intelligent robotic systems. Many studies have been focused on the obstacle avoidance to prevent “too close” or “too far” to obstacles, but difficult to obtain an optimal path. In this project, a novel biologically inspired neural network methodology to real-time collision-free navigation of an autonomous mobile robot with safety consideration in a nonstationary environment is proposed. The real-time robot path is planned through the varying neural activity landscape that represents the dynamic environment. The proposed model for robot path planning with safety consideration is capable of planning a real-time “comfortable” path overcoming the either “too close” or “too far” shortcoming. The effectiveness and efficiency are demonstrated through simulation studies that the proposed approach is capable of performing collision-free and safe navigation of an autonomous mobile robot. The implementation of such navigation model on an actual mobile robot is on-going.