Development of a Multi-target Search-and-rescue Robot Based on Improved Algorithms
Yubo Zhang · Applied and Computational Engineering · 2025
Nowadays more and more natural disasters and accidents happen around us, resulting in situations where people are trapped in danger. These areas often have complex terrains or high-risk environments. Rescue robots are becoming increasingly important in disaster relief and complex environment exploration. The use of rescue robots significantly improves search and rescue efficiency and reduces risk of casualties during rescue operations in hard-to-access or dangerous areas like earthquake ruins or fire scenes. This research develops an efficient rescue robot system that integrates advanced path planning and rapid map-building technologies for multi-target rescue tasks in complex environments. The system's core consists of two main modules: one is a multi-target path planning and obstacle avoidance module that combines A* and TSP algorithms, aimed at generating the shortest path covering all rescue points; the other is a map building module based on SLAM technology, for quickly and accurately drawing environmental maps. Comprehensive validation in computer simulation environments and real miniature car testing environments has shown that the path planning module combining A* and TSP algorithms can successfully plan the shortest rescue routes. Meanwhile, SLAM technology demonstrates its high accuracy and real-time performance in map building. The real miniature car's test results further confirm the system's feasibility and stability. This project offers a method to optimize the path planning of traditional rescue robots, potentially improving the efficiency of multi-target rescue missions. Additionally, the experimental results provide guidance and suggestions for the design, development, and deployment of actual rescue robots in the future.