On intelligent automation systems
Doctoral thesis, 2024
Developing automation systems that are capable of handling dynamic and
unpredictable situations is a challenging task, as it requires adapting to a
changing environment and managing potentially unforeseen action outcomes.
In contrast to traditional automation, where control code is explicitly pro-
grammed, a model-based approach might be a more appropriate solution for
automating such systems. Such an approach allows for integrating planning
algorithms, which can enable the generation of control sequences that consider
the system’s state. This capability is essential in enabling human-robot col-
laboration and handling error recovery and restart. We refer to such a model-
based and goal-oriented approach to automation as Intelligent Automation
Systems (IAS). To bridge the gap between research and practical utilization,
this thesis aims to facilitate the development of IAS by investigating methods
for their preparation, control, and testing.
A framework for preparation and virtual commissioning of IAS is presented,
which compiles the necessary methods into a high-level structure, aiming to
streamline the IAS development process. As part of the preparation process,
an effort to explain the unsolvability of some planning problems by localiz-
ing potential faults in behavior models is presented. Furthermore, this thesis
investigates planning and SAT solving methods aimed at improving the effi-
ciency of planning, thereby enhancing the responsiveness and adaptability of
IAS. A planning and execution framework for IAS is presented, with a focus on
handling dynamic and unpredictable systems. Finally, an iterative method for
the verification of IAS is presented, where methods such as supervisory con-
trol theory, model checking, unit and integration testing, and property-based
testing play key roles in ensuring the correct behavior of IAS. Connected to
verification, a criterion for assessing the test coverage of IAS is presented.
This research contributes to the field of intelligent automation by providing
solutions for the development, control, and verification of systems designed for
complex and unpredictable environments, aiming to bridge the gap between
theory and practice.
unpredictable situations is a challenging task, as it requires adapting to a
changing environment and managing potentially unforeseen action outcomes.
In contrast to traditional automation, where control code is explicitly pro-
grammed, a model-based approach might be a more appropriate solution for
automating such systems. Such an approach allows for integrating planning
algorithms, which can enable the generation of control sequences that consider
the system’s state. This capability is essential in enabling human-robot col-
laboration and handling error recovery and restart. We refer to such a model-
based and goal-oriented approach to automation as Intelligent Automation
Systems (IAS). To bridge the gap between research and practical utilization,
this thesis aims to facilitate the development of IAS by investigating methods
for their preparation, control, and testing.
A framework for preparation and virtual commissioning of IAS is presented,
which compiles the necessary methods into a high-level structure, aiming to
streamline the IAS development process. As part of the preparation process,
an effort to explain the unsolvability of some planning problems by localiz-
ing potential faults in behavior models is presented. Furthermore, this thesis
investigates planning and SAT solving methods aimed at improving the effi-
ciency of planning, thereby enhancing the responsiveness and adaptability of
IAS. A planning and execution framework for IAS is presented, with a focus on
handling dynamic and unpredictable systems. Finally, an iterative method for
the verification of IAS is presented, where methods such as supervisory con-
trol theory, model checking, unit and integration testing, and property-based
testing play key roles in ensuring the correct behavior of IAS. Connected to
verification, a criterion for assessing the test coverage of IAS is presented.
This research contributes to the field of intelligent automation by providing
solutions for the development, control, and verification of systems designed for
complex and unpredictable environments, aiming to bridge the gap between
theory and practice.
automated planning
Automation
testing and coverability.
modeling
virtual preparation and commissioning
robot operating system
SB-H5
Opponent: Fredrik Danielsson
Author
Endre Erös
Chalmers, Electrical Engineering, Systems and control
Endre Erős, Martin Dahl, Kristofer Bengtsson, Petter Falkman and Knut Åkesson, “Virtual preparation and commissioning of ROS2 based intelligent automation systems”
Fault localization for intelligent automation systems
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA,; Vol. 2023-September(2023)
Paper in proceeding
Evaluation of high level methods for efficient planning as satisfiability
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA,; Vol. 26(2021)
Paper in proceeding
Sequence Planner: A Framework for Control of Intelligent Automation Systems
Applied Sciences (Switzerland),; Vol. 12(2022)
Journal article
Structural Coverability for Intelligent Automation Systems
IEEE International Conference on Automation Science and Engineering,; Vol. 2023-August(2023)
Paper in proceeding
In the fast-paced world of discrete production, final assembly and material handling face numerous challenges like shorter product lifetimes, just-in-time manufacturing, and increased product complexity. This work discusses developing automation systems capable of handling such challenges.
Unlike traditional automation, which relies on explicitly programmed control code, the author discusses a model-based approach termed Intelligent Automation Systems (IAS), which can integrate planning algorithms to generate control sequences online. Such an approach allows IAS to change the pace of production, adapt to different products and manufacturing scenarios, react to dynamic environments, and enable safe human-robot interaction.
To practically address the challenges of modern discrete production, a proposed solution involves the cooperation of Autonomous Mobile Robots (AMRs), collaborative robots, and human operators within a shared workspace. AMRs transport equipment, collaborative robots perform assembly tasks, and human operators handle intricate operations and balance the workload.
To bridge the gap between research and practical application, this thesis focuses on the preparation, control, and testing methods for IAS. A framework for preparation and virtual commissioning is introduced to streamline the development process. This work also addresses the unsolvability of some planning problems by identifying faults in behavior models during the preparation process, and investigates planning and satisfiability solving methods to enhance the efficiency of planning, making IAS more responsive and adaptable to dynamic scenarios. A planning and execution framework for IAS, with a specific emphasis on handling unpredictable systems, is presented. Additionally, an iterative method for verifying IAS is introduced, which is concluded with a criterion for assessing the test coverage of IAS.
Overall, this research contributes to the field of intelligent automation by providing solutions for the development, control, and verification of systems designed to operate in complex, collaborative, and unpredictable environments, aiming to bridge the gap between theory and practical implementation.
Unlike traditional automation, which relies on explicitly programmed control code, the author discusses a model-based approach termed Intelligent Automation Systems (IAS), which can integrate planning algorithms to generate control sequences online. Such an approach allows IAS to change the pace of production, adapt to different products and manufacturing scenarios, react to dynamic environments, and enable safe human-robot interaction.
To practically address the challenges of modern discrete production, a proposed solution involves the cooperation of Autonomous Mobile Robots (AMRs), collaborative robots, and human operators within a shared workspace. AMRs transport equipment, collaborative robots perform assembly tasks, and human operators handle intricate operations and balance the workload.
To bridge the gap between research and practical application, this thesis focuses on the preparation, control, and testing methods for IAS. A framework for preparation and virtual commissioning is introduced to streamline the development process. This work also addresses the unsolvability of some planning problems by identifying faults in behavior models during the preparation process, and investigates planning and satisfiability solving methods to enhance the efficiency of planning, making IAS more responsive and adaptable to dynamic scenarios. A planning and execution framework for IAS, with a specific emphasis on handling unpredictable systems, is presented. Additionally, an iterative method for verifying IAS is introduced, which is concluded with a criterion for assessing the test coverage of IAS.
Overall, this research contributes to the field of intelligent automation by providing solutions for the development, control, and verification of systems designed to operate in complex, collaborative, and unpredictable environments, aiming to bridge the gap between theory and practical implementation.
Systematic testing of cyber-physical systems (SyTeC)
Swedish Research Council (VR) (2016-06204), 2017-01-01 -- 2022-12-31.
Virtual Commissioning of Vehicle Maintenance Operations, UNIFICATION
VINNOVA (2017-02245), 2017-06-01 -- 2020-05-31.
EUREKA ITEA3 AIToC
VINNOVA (2020-01947), 2020-10-01 -- 2023-09-30.
UNICORN - Sustainable, Peaceful and Efficient Robotic Refuse Handling
Volvo Group, 2021-03-24 -- 2021-05-31.
VINNOVA (2017-03055), 2017-10-25 -- 2020-08-31.
AIHURO-Intelligent human-robot collaboration
VINNOVA (2022-03012), 2023-02-01 -- 2026-01-31.
Subject Categories
Embedded Systems
Robotics
ISBN
978-91-8103-003-7
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5461
Publisher
Chalmers
SB-H5
Opponent: Fredrik Danielsson