Agent-Based Autonomous Systems and Abstraction Engines: Theory Meets Practice

Dennis, Louise A.; Aitken, Jonathan M.; Collenette, Joe; Cucco, Elisa; Kamali, Maryam; McAree, Owen; Shaukat, Affan; Atkinson, Katie; Gao, Yang; Veres, Sándor M.; Fisher, Michael

doi:10.1007/978-3-319-40379-3_8

Cited by 9 publications

(16 citation statements)

References 15 publications

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“…A recent example of this operation is shown in handling nuclear material [18,74] for a robot arm. In this case, an sEnglish agent is developed and linked to an ROS network, in one case controlling a KUKA IIWA manipulator.…”

Section: Connecting the Ra To Rosmentioning

confidence: 99%

“…In this paper, we are concerned with the high-level software components responsible for decisions in an AV capable of navigation, obstacle detection and avoidance, and autonomous parking. These logic-based decisions can either be implemented through a rational agent [9,10,[16][17][18][19] or through fuzzy logic [20][21][22] depending on the level of performance guarantee required.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

Al-Nuaimi

Wibowo

et al. 2021

JSAN

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The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

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Section: Connecting the Ra To Rosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

Al-Nuaimi

Wibowo

et al. 2021

JSAN

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“…Dennis et al explored the use of rational agents implemented with GWENDOLEN and several robotic applications [25]. A key feature of their implementation was the use of an "abstraction engine" for performing the translation between the agent, the "physical engine" and the "continuous engine", which were responsible for the interface with the real world (or simulated) sensors and actuators of the robot.…”

Section: Bdi For Robotic Applcationsmentioning

confidence: 99%

Toward Campus Mail Delivery Using BDI

Onyedinma

Gavigan

Esfandiari

2020

JSAN

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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.

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“…Dennis et al explored using rational agents, implemented with GWENDOLEN, for several robotic applications [58]. A key feature of their implementation was the use of an "Abstraction Engine", which translated between the agent and the robot's sensors and actuators.…”

Section: Abstraction Enginesmentioning

confidence: 99%

“…The Abstraction Engine project provides a framework and design guidance for how an agent connects to sensors and actuators. This is beyond simply providing a connection to ROS, it provides guidance on how to connect to the hardware, and was demonstrated on multiple simulated and real robotic platforms [58,59]. The JaCaROS project used ROS for connecting an agent to sensors and actuators, though guidance on how to setup those sensors and actuators was not apparent [51].…”

Section: Flexibilitymentioning

confidence: 99%

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

Gavigan¹

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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.

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Agent-Based Autonomous Systems and Abstraction Engines: Theory Meets Practice

Cited by 9 publications

References 15 publications

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

Toward Campus Mail Delivery Using BDI

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

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