Abstract
Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture tend to be mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. In this paper, we discuss how we linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use casesand demonstrated the system performance in an analogue environment. ROS handles the connections to the low-level sensors and actuators, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is sent to the reasoning system as perceptions using ROS. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration and implements a subset of the use cases required for the full system. We demonstrated the performance of the system in an analogue environment.
Highlights
An autonomous agent can be defined as a system that pursues its own agenda, affecting what it senses in the future, by sensing the environment and acting on it over time [1]
We describe how we built our autonomous robot that uses BDI and Robot Operating System (ROS) to eventually deliver interoffice mail in the Carleton University campus tunnels
The agent behaviour is defined by an AgentSpeak file which is parsed by the reasoning system at start-up, making this module fully platform agnostic: there are no assumptions about the underlying hardware, capabilities, or mission of the agent in the implementation of this system
Summary
An autonomous agent can be defined as a system that pursues its own agenda, affecting what it senses in the future, by sensing the environment and acting on it over time [1]. Being underground means that access to Global Navigation Satellite System (GNSS) signals, such as Global Positioning System (GPS), is not possible, and internet access is limited to certain areas In this context, the robot will have to know where it is and where to deliver mail, but there are some sub-goals, like obstacle avoidance and battery recharge, which it might have to achieve in order to get to its main goal. BDI provides a good goal-oriented agent architecture that is resilient to plan failure and changes to context. It supports the notion of shorter-term and longer-term plans that can be organized so as not to conflict with each other. Additional details with respect to the hardware implementation, related to our connections between our computer and the robot’s power system and our line sensor circuit, used for path following, are provided (Appendix A)
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