Abstract. This study presents a method for improving the safety of autonomous navigation of a mobile robotic platform through multi-sensor data fusion. The main objective is to reliably estimate the robot’s position and generate a safe motion trajectory in the presence of obstacles by integrating data obtained from LiDAR, an RGB camera, an inertial measurement unit (IMU), and wheel encoders. The proposed method integrates the dynamic model of the mobile platform, a multi-sensor data fusion algorithm, an environmental assessment module, safe trajectory planning, and real-time motion control within a unified framework. The dynamic model accounts for the robot’s motion parameters, wheel angular velocities, and control inputs. Based on the fused sensor information, the current position and motion state of the robot are estimated, and the resulting data are transferred to the trajectory planning and control system. During safe trajectory generation, the minimum distance between the robotic platform and surrounding obstacles is considered to determine a motion direction with a reduced risk of collision. The effectiveness of the proposed method was evaluated using MATLAB and ROS–Gazebo simulation environments. The experiments investigated the robot’s motion along a predefined route, detection of static obstacles, and their safe avoidance. In addition, external noise was introduced into the sensor measurements to evaluate the robustness of the proposed approach to measurement errors. The results demonstrated that multi-sensor data fusion improves the stability of robot position estimation, enables consistent trajectory tracking, and contributes to safer autonomous navigation. The proposed method can be applied to the development of autonomous navigation systems for mobile robots in industrial automation, warehouse logistics, and service robotics.

Keywords: mobile robotic platform, safe navigation, multi-sensor data fusion, LiDAR, RGB camera, IMU, wheel encoder, dynamic model, trajectory planning, ROS–Gazebo.