US: Robot dog gets AI power to carry out rescue missions effectively

Texas A&M University engineering students have developed a memory-based navigation framework that significantly enhances the capabilities of AI-powered robotic dogs for rescue missions. Unlike traditional robots that merely follow commands, this system enables the robot to see, remember locations, and make real-time decisions using a multimodal large language model (MLLM) that integrates visual input, voice commands, and advanced path planning. The robot can navigate chaotic, GPS-denied environments such as disaster zones by recalling previously traveled routes, avoiding obstacles instantly, and employing high-level reasoning to optimize its movements. This approach represents a novel integration of visual memory and language-model-based navigation within a modular platform, improving efficiency and adaptability in unpredictable settings. Supported by the National Science Foundation, the team demonstrated how the robot’s AI blends reactive behaviors with deliberate planning, making it a smarter, more intuitive partner for search-and-rescue teams, emergency crews, and disaster response units. Beyond emergency applications, the technology holds promise for broader use cases including hospital and warehouse operations, mobility