An Architecture for Integrating BDI Agents with a Simulation Environment

Davoust, Alan; Gavigan, Patrick; Ruiz-Martín, Cristina; Trabes, Guillermo G.; Esfandiari, Babak; Wainer, Gabriel; James, Jeremy J.

doi:10.1007/978-3-030-51417-4_4

Cited by 6 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the authors' own related work includes the Simulated Autonomous Vehicle Infrastructure (SAVI) project, which aimed to develop an architecture for simulating autonomous agents implemented using a Jason BDI [8] [7]. Among its key features is the decoupling of the agent reasoning cycle from the simulation time cycle, enabling the simulated agents to run in their own time.…”

Section: Related Workmentioning

confidence: 99%

“…The control software is implemented using a set of modules connected via ROS, as shown in figure 2. The reasoning system for this robot, inspired by the SAVI project [8,7], decouples the reasoning cycle from the interface to the sensors and actuators using a state synchronization module. The internal reasoning system for this project, called SAVI ROS BDI, and shown in figure 3, is inspired by the original SAVI configuration.…”

Section: Softwarementioning

confidence: 99%

See 1 more Smart Citation

Toward Campus Mail Delivery Using BDI

Onyedinma

Gavigan

Esfandiari

2020

Electron. Proc. Theor. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually 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. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. 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 hardwaresoftware integration, and implements a subset of the use cases required for the full system.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

Toward Campus Mail Delivery Using BDI

Onyedinma

Gavigan

Esfandiari

2020

Electron. Proc. Theor. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors' own related work includes the Simulated Autonomous Vehicle Infrastructure (SAVI) project, which aimed to develop an architecture for simulating autonomous agents implemented using a Jason BDI [27,28]. Among its key features is the decoupling of the agent reasoning cycle from the simulation time cycle, enabling the simulated agents to run in their own time.…”

Section: Bdi For Robotic Applcationsmentioning

confidence: 99%

“…The control software is implemented using a set of modules connected via ROS, as shown in Figure 2. The reasoning system for this robot, inspired by the SAVI project [27,28], decouples the reasoning cycle from the interface to the sensors and actuators using a state synchronization module. The internal reasoning system for this project, called SAVI ROS BDI, and shown in Figure 3, is inspired by the original SAVI configuration.…”

Section: Softwarementioning

confidence: 99%

Toward Campus Mail Delivery Using BDI

Onyedinma

Gavigan

Esfandiari

2020

JSAN

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It provided the Simulated Autonomous Vehicle Infrastructure (SAVI), an open source architecture for integrating BDI agents with a simulation platform. This allows for separation of concerns between the development of complex multi-agent behaviours and simulated environments in which to to test them [7]. Building on the work with SAVI, an integration of this concept with ROS was developed and integrated with an iRobot Create2 robot as a prototype for mail delivery in the Carleton University tunnels.…”

Section: Contributions and Publicationsmentioning

confidence: 99%

Agent in a Box: A Framework for Autonomous Mobile Robots with Beliefs, Desires, and Intentions

Gavigan¹

Self Cite

View full text Add to dashboard Cite

This thesis investigates the use of Beliefs-Desires-Intentions (BDI) for controlling autonomous mobile robots. Autonomous systems should ideally be designed for flexibility so that they can intelligently react to ever-changing environments and operational conditions. If they are given such flexibility, they can accept goals and set a path to achieve these goals in a self-responsible manner while displaying some form of intelligence. Developers of autonomous mobile robotics are interested in how to program mobile robots while guaranteeing reliability and resilience.In investigating the use of BDI for controlling autonomous mobile robots, the Agent in a Box framework was developed. This framework includes a general architecture for connecting a Jason agent to the environment using Robot Operating System (ROS). This connection is done in a way that is flexible to a variety of application domains that use different sensors and actuators.The Agent in a Box provides the needed customization to the agent's reasoner to ensure that the agent's behaviours are properly prioritized. Generic plans for behaviours that are common to a variety of mobile robots are also provided. These include plans for resource management and for navigation, which generates a route to a destination in the form of a plan which can be monitored and interrupted by the reasoner if needed. These components allow developers, for specific application domains, to focus on domain-specific code. Agents implemented using the Agent in a Box are rational, mission capable, safety conscious, fuel autonomous, and understandable.The Agent in a Box was used to demonstrate the capability of BDI agents to control robots in a variety of application domains through a set of case studies: a grid environment, a simulated autonomous car, and a prototype mail delivery robot. These case studies demonstrated that the agent was able to successfully control the robots in all of the application domains tested, including when it was run on a Raspberry Pi computer. By implementing the robots with the Agent in a Box, the development burden was reduced because the framework provided generic behaviours that could be used by each of the agents. The Agent in a Box was also found to enforce good software engineering practices, resulting in a noticeable improvement in runtime performance compared to other approaches that did not use the Agent in a Box. iv v Acknowledgements I would like to thank my supervisor, Babak Esfandiari, and the members of my evaluation committee for their time, effort, support, and advice. • • •Thank you to my family, especially to my wife Catharina and my parents Jacqueline and Neil for all of the help and support. To my daughters, Gemma and Maeve, who were both born while I was working on this degree, thank you for being small and adorable, even if you did not make my progress on this thesis go any faster. • • •I would also like to thank a number of colleagues who have provided help along the way.

show abstract

An Architecture for Integrating BDI Agents with a Simulation Environment

Cited by 6 publications

References 18 publications

Toward Campus Mail Delivery Using BDI

Toward Campus Mail Delivery Using BDI

Toward Campus Mail Delivery Using BDI

Agent in a Box: A Framework for Autonomous Mobile Robots with Beliefs, Desires, and Intentions

Contact Info

Product

Resources

About