Embedding Autonomous Agents in Resource-Constrained Robotic Platforms

TL;DR

This paper demonstrates that integrating high-level autonomous agents with resource-constrained robots enables real-time decision-making, allowing small embedded systems to perform autonomous maze exploration efficiently.

Contribution

It introduces a method for embedding AgentSpeak-based autonomous agents into small robots, showing real-time decision-making in resource-limited environments.

Findings

Successfully solved maze in 59 seconds

Reasoning cycles under one millisecond

Real-time autonomous operation achieved

Abstract

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.