在小型机器人上实现自主决策，1秒内完成一次判断
Embedding Autonomous Agents in Resource-Constrained Robotic Platforms
- 用AgentSpeak语言让小车自主规划路径
- 59秒解迷宫，仅用287次推理，每次决策<1毫秒
- 适合嵌入式机器人实时控制研究者参考
许多嵌入式设备在资源受限且动态变化的环境中运行，需具备本地决策能力。本文将基于AgentSpeak编程的自主智能体与一台小型两轮机器人结合，使其利用自身感知数据探索迷宫。实验表明，该智能体在59秒内完成迷宫求解，共执行287次推理周期，每次决策耗时不足1毫秒。结果证明，该推理过程足够高效，可在资源受限硬件上实现实时运行。该集成展示了高层代理式控制在资源受限嵌入式系统中实现自主操作的可行性。
原文摘要 · Abstract (English)
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.
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