Navigation and steering control of a service robot using Kinectv2 and LiDAR

Abstract

Service robots operating in complex indoor environments with dynamic obstacles and narrow spaces require accurate mapping, localization, navigation, and motion control for safe and efficient operation. However, integrated systems that combine all these processes within a unified framework remain limited. This study proposes an integrated navigation and steering control system using RPLIDAR A3 and Kinect v2 sensors within a ROS2-based PLAN framework. The proposed system combines GMapping SLAM and RTAB-Map for hybrid mapping, extended Kalman filter (EKF) for localization enhancement, bidirectional RRT* for global path planning, artificial potential field (APF) for local obstacle avoidance, and a Type-2 fuzzy logic controller (FLC) for steering control. Experimental results show that the mapping system achieved a mean squared error (MSE), F1-score, recall, and precision of 0.26, 0.1714, 0.0312, and 0.0498, respectively. In steering control evaluation, the proposed Type-2 FLC with seven membership functions outperformed the proportional–integral–derivative (PID) controller under the tested conditions, achieving a rise time of 0.2901 s, settling time of 1.6819 s, peak time of 1.6997 s, overshoot of 0.4167%, and steady-state error of 10 (3.6). These results indicate that the proposed system improves navigation stability, steering performance, and environmental perception, demonstrating strong potential for autonomous service robot applications in complex indoor environments.