Toward Campus Mail Delivery Using BDI

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.

An Overview of Verification and Validation Challenges for Inspection Robots

“…Traditionally, agent programming languages have adopted the well known Belief-Desire-Intention (BDI) model [37,38] to reason about the following mental attitudes: beliefs that describe the agent's knowledge about the world; desires that correspond to the goals that the agent wants to achieve; and intentions that represent the commitment and know-how of the agent to achieve a particular goal. BDI agents have been successfully applied to perform high-level decision making in robots, as evidenced recently by [39][40][41][42][43]. In addition to BDI agents, other intensively-studied symbolic reasoning approaches include temporal reasoning [44], strategic reasoning [45][46][47][48][49], epistemic reasoning [50][51][52][53][54][55], and trust reasoning [43].…”

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

“…In [10,11], Jason is linked with ROS through their SAVI ROS BDI architecture. This architecture is implemented in Java (using the rosjava package, and as such has the same disadvantage present in [8]) and mainly introduces a state synchronisation module that acts as a mediator between ROS and Jason by managing perceptions, incoming and outgoing messages, and actions being sent by the agent.…”

Special Issue: Agents and Robots for Reliable Engineered Autonomy

“…Paper [1] Paper [2] Paper [3] Paper [4] Paper [5] We hope that the different research communities that are represented in our Special Issue will improve their collaboration efforts in the future so that the best proposals from these different areas can be combined to create new and exciting solutions and tools to be exploited both in academia and in industry.…”

“…This Special Issue was created based on the topics discussed at the First Workshop on Agents and Robots for reliable Engineered Autonomy (https://area2020.github.io/ accessed on 30 June 2021) (AREA 2020). One of the accepted papers in the workshop was invited to extend their work and underwent additional peer-review evaluation before being accepted for publication [2]. Paper [2] contains details about an architecture for linking robots with autonomous agents applied to a case study of campus mail delivery.…”

“…One of the accepted papers in the workshop was invited to extend their work and underwent additional peer-review evaluation before being accepted for publication [2]. Paper [2] contains details about an architecture for linking robots with autonomous agents applied to a case study of campus mail delivery.…”