Embedding Autonomous Agents in Resource-Constrained Robotic Platforms
This work addresses the need for responsive, independent operation in embedded systems like robots, though it is incremental as it applies an existing agent method to a new robotic context.
The paper tackled the problem of enabling autonomous decision-making on resource-constrained robotic platforms by integrating an AgentSpeak-based agent with a small robot to explore a maze, resulting in successful maze-solving in 59 seconds with 287 reasoning cycles and decision phases under one millisecond.
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.