Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion

Obstacle avoidance is a critical component of the navigation stack required for mobile robots to operate effectively in complex and unknown environments. In this research, three end-to-end Convolutional Neural Networks (CNNs) were trained and evaluated offline and deployed on a differential-drive mobile robot for real-time obstacle avoidance to generate low-level steering commands from synchronized color and depth images acquired by an Intel RealSense D415 RGB-D camera in diverse environments. Offline evaluation showed that the NetConEmb model achieved the best performance with a notably low MedAE of $0.58\times {10}^{-3}$ rad/s. In comparison, the lighter NetEmb architecture, which reduces the number of trainable parameters by approximately 25% and converges faster, produced comparable results with an RMSE of $21.68\times {10}^{-3}$ rad/s, close to the $21.42\times {10}^{-3}$ rad/s obtained by NetConEmb. Real-time navigation further confirmed NetConEmb’s robustness, achieving a 100% success rate in both known and unknown environments, while NetEmb and NetGated succeeded only in navigating the known environment.