Many embedded devices operate under resource constraints and in dynamic environments, requiring local decision-making capabilities. Enabling devices to make independent decisions in such environments can improve the responsiveness of the system and reduce the dependence on constant external control. In this work, we integrate an autonomous agent, programmed using AgentSpeak, with a small two-wheeled robot that explores a maze using its own decision-making and sensor data. Experimental results show that the agent successfully solved the maze in 59 seconds using 287 reasoning cycles, with decision phases taking less than one millisecond. These results indicate that the reasoning process is efficient enough for real-time execution on resource-constrained hardware. This integration demonstrates how high-level agent-based control can be applied to resource-constrained embedded systems for autonomous operation.

翻译：许多嵌入式设备在资源受限和动态环境中运行，需要具备本地决策能力。使设备在此类环境中能够做出独立决策，可以提高系统的响应能力，并减少对持续外部控制的依赖。在本研究中，我们将一个使用AgentSpeak编程的自主智能体集成到一个小型双轮机器人上，该机器人利用自身的决策能力和传感器数据探索迷宫。实验结果表明，该智能体在59秒内成功解决了迷宫问题，使用了287个推理周期，决策阶段耗时不到一毫秒。这些结果表明，推理过程足够高效，可以在资源受限的硬件上实时执行。这一集成展示了如何将基于智能体的高层控制应用于资源受限的嵌入式系统，以实现自主运行。