作者:
Josh LuzierDavid C. ConnerDepartment of Physics
Computer Science and Engineering Capable Humanitarian Robotics and Intelligent Systems Lab (CHRISLab) Christopher Newport University Newport News Virginia
This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically p...
This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically precise specifications to generate “correct-by-construction” behavior controllers. Our approach builds upon work using the GR(1) (General Reactivity of rank 1) fragment of LTL to synthesize a reactive hierarchical finite state machine that can be directly executed in FlexBE. The presented work expands on previous ROS 1 tools, and extends them to ROS 2 in a way that supports more general specifications that incorporate environmental states. This paper presents an accessible open-source demonstration that serves as a general introduction to these powerful synthesis techniques.
Hierarchical Finite State Machines (HFSM) are commonly used as high-level behavioral control strategies for robotic systems. Over the years a number of behavior engines have been developed for the original Robot Opera...
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Hierarchical Finite State Machines (HFSM) are commonly used as high-level behavioral control strategies for robotic systems. Over the years a number of behavior engines have been developed for the original Robot Operating System (ROS 1), including the popular Flexible Behavior Engine (FlexBE). In recent years a new ROS 2 system has been developed to improve communication performance by using the Data Distribution Service (DDS) protocol. This paper describes the conversion of both FlexBE and the open-source Flexible Navigation system to the latest ROS 2 release, and our experiments with the new system. Both ROS 2 conversions are released as open-source and available for use by the robotics community.
The Robot Operating System (ROS) is frequently used to develop complex robotic systems with many interacting software components. While ROS provides several tools for visualizing active ROS deployments, it lacks tools...
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The Robot Operating System (ROS) is frequently used to develop complex robotic systems with many interacting software components. While ROS provides several tools for visualizing active ROS deployments, it lacks tools for Model-Based Engineering (MBE) during the development stage, and ROS lacks tools for easily documenting the interfaces and deployed components other than specific configuration and launch files themselves. This paper presents a software tool chain - dubbed CHRIS ROS Modeling - that enables users to document an existing ROS deployment. The information is stored as instances of a simplified metamodel in portable formats. The resulting information serves to document an existing system and will serve as the foundation for future work to develop an integrated MBE system. This paper compares our approach to similar approaches currently in development.
Hierarchical Finite State Machines (HFSMs) continue to be a popular strategy for high-level behavioral control in robotics. Within the Robot Operating System (ROS) ecosystem, the Flexible Behavior Engine (FlexBE) prov...
Hierarchical Finite State Machines (HFSMs) continue to be a popular strategy for high-level behavioral control in robotics. Within the Robot Operating System (ROS) ecosystem, the Flexible Behavior Engine (FlexBE) provides an easy to use Python implementation with an intuitive user interface for runtime monitoring and "collaborative autonomy." In this paper, we describe the latest evolution of the FlexBE UI, that leverages FastAPI to provide a Python-based native ROS node interface, to a web-based user interface. Additionally, the paper describes a "quick-start" introductory tutorial to state machines using FlexBE. This system provides a natural introduction to HFSM-based techniques.
In this work, we take a step towards bridging the gap between the theory of formal synthesis and its application to real-world, complex, robotic systems. In particular, we present an end-to-end approach for the automa...
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ISBN:
(纸本)9781467380270
In this work, we take a step towards bridging the gap between the theory of formal synthesis and its application to real-world, complex, robotic systems. In particular, we present an end-to-end approach for the automatic generation of code that implements high-level robot behaviors in a verifiably correct manner, including reaction to the possible failures of low-level actions. We start with a description of the system defined a priori. Thus, a non-expert user need only specify a high-level task. We automatically construct a formal specification, in a fragment of Linear Temporal Logic (LTL), that encodes the system's capabilities and constraints, the task, and the desired reaction to low-level failures. We then synthesize a reactive mission plan that is guaranteed to satisfy the formal specification, i.e., achieve the task's goals or correctly react to failures. Lastly, we automatically generate a state machine that instantiates the synthesized symbolic plan in software. We showcase our approach using Team ViGIR's software and Atlas humanoid robot and present lab experiments, thus demonstrating the application of formal synthesis techniques to complex robotic systems. The proposed approach has been implemented and open-sourced as a collection of Robot Operating System (ROS) packages, which are adaptable to other systems.
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