Skill-Based Architecture Development for Online Mission Reconfiguration and Failure Management

Albore, Alexandre; Doose, David; Grand, Christophe; Lesire, Charles; Manecy, Augustin

doi:10.1109/rose52553.2021.00015

Cited by 10 publications

(18 citation statements)

References 9 publications

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“…The system described in [14] and [1] proposes a formal language to specify robot skills with an expressive descriptive model used for reasoning, and an operational model. It maintains a life cycle for every robot skill and allows concurrent activation of the same skill.…”

Section: Planning-based Deliberative Architecturesmentioning

confidence: 99%

Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models

Wertheim

Suissa

Brafman

2022

Electron. Proc. Theor. Comput. Sci.

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To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical robot's actions and sensors, and the limited information it has. In principle, AI planning can be used to address this challenge and generate good behavior policies automatically. But this requires passing three hurdles. First, the AI must understand each skill's impact on the world. Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code. Third, much integration effort is required to tie together all components. We describe an approach for integrating robot skills into a working autonomous robot controller that schedules its skills to achieve a specified task and carries four key advantages. 1) Our Generative Skill Documentation Language (GSDL) makes code documentation simpler, compact, and more expressive using ideas from probabilistic programming languages. 2) An expressive abstraction mapping (AM) bridges the gap between low-level robot code and the abstract AI planning model. 3) Any properly documented skill can be used by the controller without any additional programming effort, providing a Plug'n Play experience. 4) A POMDP solver schedules skill execution while properly balancing partial observability, stochastic behavior, and noisy sensing.

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Section: Planning-based Deliberative Architecturesmentioning

confidence: 99%

Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models

Wertheim

Suissa

Brafman

2022

Electron. Proc. Theor. Comput. Sci.

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“…The main advantage of such architectures are their modularity, easy reconfiguration and repurposability, as well as making robotic programming easier for end users. The skills are then composed to create more complex tasks to achieve specific goals or missions, while using feedback from sensors and actuators [30,1,36,35,18]. The mission design using skill-based architecture is often coupled with deliberative functions to create autonomous systems capable of adapting to their environment or faulty behaviors, often using model-based architectures [16,29].…”

Section: Related Workmentioning

confidence: 99%

“…Ingrand [15] gathers the state of the art of V&V formal methods for autonomous systems. Albore [1] shows how missions can be designed through Behavior Trees, allowing for robust mission specification and fault management, however such method restricts the verification of the system to a specific mission. Evans [8] proposes a Model-Based Mission Assurance approach to improve the safety and robustness of a system in the early development stage, using Assurance models synthesis, implementing fault trees and Bayesian nets as inputs for SysML diagrams, opening interesting perspectives for the implementation of fault management into software architecture.…”

Section: Related Workmentioning

confidence: 99%

“…The software architectures tackled in this work are skill-based architectures, also called task-based [21,1,30], where the robotic system is decomposed into elementary actions. This approach usually comes with a high-level of abstraction of the software and hardware of the system, while offering a wide range of specification possibilities.…”

Section: Introductionmentioning

confidence: 99%

“…A controller layer is then implemented, to test the architecture and control the system. The architecture used for this work will be the Skillset formalization of Albore et al [1], which is composed of resources, skills and events and can model the robot as well as its environment and operator decisions. The strength of this formalization is that a code generating tool is provided, which will guarantee that the execution of the system will be as specified in the skillset.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SkiNet, A Petri Net Generation Tool for the Verification of Skillset-based Autonomous Systems

Pelletier

Lesire

Doose

et al. 2022

Electron. Proc. Theor. Comput. Sci.

Self Cite

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The need for high-level autonomy and robustness of autonomous systems for missions in dynamic and remote environment has pushed developers to come up with new software architectures. A common architecture style is to summarize the capabilities of the robotic system into elementary actions, called skills, on top of which a skill management layer is implemented to structure, test and control the functional layer. However, current available verification tools only provide either mission-specific verification or verification on a model that does not replicate the actual execution of the system, which makes it difficult to ensure its robustness to unexpected events. To that end, a tool, SkiNet, has been developed to transform the skill-based architecture of a system into a Petri net modeling the state-machine behaviors of the skills and the resources they handle. The Petri net allows the use of model-checking, such as Linear Temporal Logic (LTL) or Computational Tree Logic (CTL), for the user to analyze and verify the model of the system.

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Nautilus: An autonomous surface vehicle with a multilayer software architecture for offshore inspection

Campos,

Gonçalves,

Campos

et al. 2024

Journal of Field Robotics

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The increasing adoption of robotic solutions for inspection tasks in challenging environments is becoming increasingly prevalent, particularly in the offshore wind energy industry. This trend is driven by the critical need to safeguard the integrity and operational efficiency of offshore infrastructure. Consequently, the design of inspection vehicles must comply with rigorous requirements established by the offshore Operation and Maintenance (O&M) industry. This work presents the design of an autonomous surface vehicle (ASV), named Nautilus, specifically tailored to withstand the demanding conditions of offshore O&M scenarios. The design encompasses both hardware and software architectures, ensuring Nautilus's robustness and adaptability to the harsh maritime environment. It presents a compact hull capable of operating in moderate sea states (wave height up to 2.5 m), with a modular hardware and software architecture that is easily adapted to the mission requirements. It has a perception payload and communication system for edge and real‐time computing, communicates with a Shore Control Center and allows beyond visual line‐of‐sight operations. The Nautilus software architecture aims to provide the necessary flexibility for different mission requirements to offer a unified software architecture for O&M operations. Nautilus's capabilities were validated through the professional testing process of the ATLANTIS Test Center, involving operations in both near‐real and real‐world environments. This validation process culminated in Nautilus's reaching a Technology Readiness Level 8 and became the first ASV to execute autonomous tasks at a floating offshore wind farm located in the Atlantic.

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Skill-Based Architecture Development for Online Mission Reconfiguration and Failure Management

Cited by 10 publications

References 9 publications

Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models

Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models

SkiNet, A Petri Net Generation Tool for the Verification of Skillset-based Autonomous Systems

Nautilus: An autonomous surface vehicle with a multilayer software architecture for offshore inspection

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