The deployment of artificial intelligence models at the edge is increasingly critical for autonomous robots operating in GPS-denied environments where local, resource-efficient reasoning is essential. This work demonstrates the feasibility of deploying small Vision-Language Models (VLMs) on mobile robots to achieve real-time scene understanding and reasoning under strict computational constraints. Unlike prior approaches that separate perception from mobility, the proposed framework enables simultaneous movement and reasoning in dynamic environments using only on-board hardware. The system integrates a compact VLM with multimodal perception to perform contextual interpretation directly on embedded hardware, eliminating reliance on cloud connectivity. Experimental validation highlights the balance between computational efficiency, task accuracy, and system responsiveness. Implementation on a mobile robot confirms one of the first successful deployments of small VLMs for concurrent reasoning and mobility at the edge. This work establishes a foundation for scalable, assured autonomy in applications such as service robotics, disaster response, and defense operations.

翻译：在GPS拒止环境中，自主机器人需依赖本地、资源高效的计算进行推理，因此人工智能模型在边缘端的部署日益关键。本研究证明了在移动机器人上部署小型视觉-语言模型（VLM）以实现严格计算约束下的实时场景理解与推理的可行性。与以往将感知与移动分离的方法不同，所提出的框架仅利用机载硬件即可在动态环境中实现同时移动与推理。该系统将紧凑型VLM与多模态感知相结合，直接在嵌入式硬件上进行上下文解释，消除了对云连接的依赖。实验验证突出了计算效率、任务准确性与系统响应性之间的平衡。在移动机器人上的实现证实了这是首批成功部署小型VLM以在边缘端实现并发推理与移动的案例之一。这项工作为服务机器人、灾难响应和国防行动等应用中的可扩展、可靠自主性奠定了基础。