Research and Design of an Indoor Rescue Robot for Localization and Navigation

This paper conducts research on indoor rescue robot technology, focusing on an efficient positioning and navigation scheme. The control system of rescue robot is designed. The robot can find the rescue point and start working in an indoor environment with multiple target points (rescue points and obstacle points) quickly and safely. The Adaptive Monte Carlo Localization (AMCL) algorithm is adopted to achieve accurate positioning of the robot. A method combining Light Detection and Ranging (LiDAR) point cloud coordinate transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed for target point positioning, and the DBSCAN algorithm is optimized to improve the speed, accuracy and stability of positioning. The ${\mathrm {A}}^{*}$ algorithm is used for global path planning. Optimized Timed Elastic Band (TEB) algorithm is used for local path planning. By adding end smoothness constraints to TEB for algorithm optimization, the robot can adjust its travel route flexibly, reduce the impact force generated by sudden speed changes of the robot, and achieve more accurate and smooth local path planning. The proposed positioning and navigation scheme was comprehensively validated through simulation and on-site experiments. The results show that the optimized algorithm can achieve reliable positioning and efficient navigation in indoor environments than the original algorithm, which provides strong technical support for indoor rescue missions.