AI in Search and Rescue Robots: Technical Exploration of Path Planning and Autonomous Decision-Making

This article focuses on the application of AI in search and rescue robots, primarily exploring key technologies such as path planning, environmental perception, and autonomous decision-making, analyzing their value and challenges in disaster rescue scenarios, covering technology stacks, practical cases, and future development directions.

Disaster sites have harsh and dangerous environments, making it difficult for human rescuers to enter quickly, thus highlighting the value of search and rescue robots. The unique challenges they face include: environmental uncertainty (unstable structures, complex terrain), limited communication (cannot rely on remote control), time pressure (limited golden rescue time), and multi-objective trade-offs (search speed, coverage, energy consumption, etc.). In addition, the AI for Robotics 2 course assignment project reflects the importance of this technology in academic education, requiring mastery of multiple AI sub-fields such as path planning and perception fusion.

Core Technology Stack: Path planning algorithms (A*, D*, RRT/RRT+, potential field method), SLAM (filtering, graph optimization, visual SLAM), search strategies (full coverage path planning, collaborative search, adaptive search), perception and recognition (multi-sensor + AI target recognition); Autonomous Decision-Making Architecture: A hybrid architecture combining a reactive layer (fast obstacle avoidance, emergency stop) and a deliberative layer (global planning, resource management), coordinating tasks and movements through a three-layer architecture.

• 9/11 Attacks: Robot deployment verified technical feasibility but exposed insufficient communication and mobility; - Fukushima Nuclear Accident: Robots entered high-radiation areas to assess damage; - Earthquake Rescue: Assisted in locating buried survivors in the Haiti and Christchurch earthquakes, demonstrating application value in environments inaccessible to humans.

Current Challenges: Mobility limitations (difficulty moving in complex ruins), perception robustness (harsh conditions affecting sensors), human-robot collaboration (interfaces need improvement), energy limitations (short battery life); Cutting-Edge Directions: Bionic robots (improving mobility in narrow spaces), swarm robots (collaborative search), human-robot symbiosis (exoskeleton collaboration), AI-enhanced perception (deep learning to improve target recognition).

Search and rescue robots are one of the most humanitarian applications of AI; technological progress can save more lives. Course assignments connect academic and practical fields, making them an ideal practice direction for AI/robotics students. In the future, search and rescue robots will be more intelligent and reliable, becoming an indispensable force in disaster rescue.