The rise of intelligent autonomous systems, especially in robotics and autonomous agents, has created a critical need for robust communication middleware that can ensure real-time processing of extensive sensor data. Current robotics middleware like Robot Operating System (ROS) 2 faces challenges with nondeterminism and high communication latency when dealing with large data across multiple subscribers on a multi-core compute platform. To address these issues, we present High-Performance Robotic Middleware (HPRM), built on top of the deterministic coordination language Lingua Franca (LF). HPRM employs optimizations including an in-memory object store for efficient zero-copy transfer of large payloads, adaptive serialization to minimize serialization overhead, and an eager protocol with real-time sockets to reduce handshake latency. Benchmarks show HPRM achieves up to 114x lower latency than ROS2 when broadcasting large messages to multiple nodes. We then demonstrate the benefits of HPRM by integrating it with the CARLA simulator and running reinforcement learning agents along with object detection workloads. In the CARLA autonomous driving application, HPRM attains 91.1% lower latency than ROS2. The deterministic coordination semantics of HPRM, combined with its optimized IPC mechanisms, enable efficient and predictable real-time communication for intelligent autonomous systems. Code and videos can be found on our project page: https://hprm-robotics.github.io/HPRM

Timing control while preserving determinism is often a key requirement for ensuring the safety and correctness of distributed cyber-physical systems (CPS). Discrete-event (DE) systems provide a suitable model of computation (MoC) for time-sensitive distributed CPS. The high-level architecture (HLA) is a useful tool for the distributed simulation of DE systems, but its techniques can be adapted for implementing distributed CPS. However, HLA incurs considerable overhead in network messages conveying timing information between the distributed nodes and the centralized run-time infrastructure (RTI). This paper gives a novel approach and implementation that reduces such network messages while preserving DE semantics. An evaluation of our runtime demonstrates that our approach significantly reduces the volume of messages for timing information in HLA.

Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which enforces a set of actors to have a fixed communication pattern. This allows the scheduler to eliminate work needed for synchronization, such as acquiring and releasing locks for each actor or sending and processing coordination-related messages. Our framework, Lingua Franca (LF), a coordination language based on the reactor model, also supports true parallelism in Python and provides a unified interface that allows users to automatically generate dataflow graphs for RL tasks. In comparison to Ray on a single-node multi-core compute platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI Gym and Atari environments, reduces the average training time of synchronized parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by 5.12x.

We discuss a novel approach for constructing deterministic reactive systems that revolves around a temporal model that incorporates a multiplicity of timelines. This model is central to Lingua Franca ( LF ), a polyglot coordination language and compiler toolchain we are developing for the definition and composition of concurrent components called reactors, which are objects that react to and emit discrete events. Our temporal model differs from existing models like the logical execution time (LET) paradigm and synchronous languages in that it reflects that there are always at least two distinct timelines involved in a reactive system; a logical one and a physical one—and possibly multiple of each kind. This article explains how the relationship between events across timelines facilitates reasoning about consistency and availability across components in cyber-physical systems (CPSs).

Complex software systems often feature distinct modes of operation, each designed to handle a particular scenario that may require the system to respond in a certain way. Breaking down system behavior into mutually exclusive modes and discrete transitions between modes is a commonly used strategy to reduce implementation complexity and promote code readability. The work in this paper aims to bring the advantages of working with modal models to mainstream programming languages, by following the polyglot coordination approach of Lingua Franca (LF), in which verbatim target code (e. g., C, C++, Python, Typescript, or Rust) is encapsulated in composable reactive components called reactors. Reactors can form a dataflow network, are triggered by timed as well as sporadic events, execute concurrently, and can be distributed across nodes on a network. With modal models in LF, we introduce a lean extension to the concept of reactors that enables the coordination of reactive tasks based on modes of operation.

Actor frameworks and similar reactive programming techniques are widely used for building concurrent systems. They promise to be efficient and scale well to a large number of cores or nodes in a distributed system. However, they also expose programmers to nondeterminism, which often makes implementations hard to understand, debug, and test. The recently proposed reactor model is a promising alternative that enables deterministic concurrency. In this article, we present an efficient, parallel implementation of reactors and demonstrate that the determinacy of reactors does not imply a loss in performance. To show this, we evaluate Lingua Franca (LF), a reactor-oriented coordination language. LF equips mainstream programming languages with a deterministic concurrency model that automatically takes advantage of opportunities to exploit parallelism. Our implementation of the Savina benchmark suite demonstrates that, in terms of execution time, the runtime performance of LF programs even exceeds popular and highly optimized actor frameworks. We compare against Akka and CAF, which LF outperforms by 1.86× and 1.42×, respectively.

In distributed applications, Brewer’s CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We extend consistency and availability to refer to cyber-physical properties such as the state of the physical system and delays in actuation. We have further extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design cyber-physical systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on the Lingua Franca coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable embedded software.

Formal verification of cyber-physical systems (CPS) is challenging because it has to consider real-time and concurrency aspects that are often absent in ordinary software. Moreover, the software in CPS is often complex and low-level, making it hard to assure that a formal model of the system used for verification is a faithful representation of the actual implementation, which can undermine the value of a verification result. To address this problem, we propose a methodology for building verifiable CPS based on the principle that a formal model of the software can be derived automatically from its implementation. Our approach requires that the system implementation is specified in Lingua Franca (LF), a polyglot coordination language tailored for real-time, concurrent CPS, which we made amenable to the specification of safety properties via annotations in the code. The program structure and the deterministic semantics of LF enable automatic construction of formal axiomatic models directly from LF programs. The generated models are automatically checked using Bounded Model Checking (BMC) by the verification engine Uclid5 using the Z3 SMT solver. The proposed technique enables checking a well-defined fragment of Safety Metric Temporal Logic (Safety MTL) formulas. To ensure the completeness of BMC, we present a method to derive an upper bound on the completeness threshold of an axiomatic model based on the semantics of LF. We implement our approach in the LF V erifier and evaluate it using a benchmark suite with 22 programs sampled from real-life applications and benchmarks for Erlang, Lustre, actor-oriented languages, and RTOSes. The LF V erifier correctly checks 21 out of 22 programs automatically.

Abstract In 2010, Fuhrmann et al. argued for enhancing modeler productivity by providing tooling that, put simply, combines the best of textual and graphical worlds. They referred to this as pragmatics , and argued that a key enabler would be the ability to automatically synthesize customized graphical views from a (possibly textual) model. The model would be the “ground truth” used, for example, for downstream code synthesis and simulation; the graphical views would typically be abstractions from the model serving various purposes, including documentation. Twelve years later, we reflect on their proposal, and illustrate the current state with the recently developed polyglot coordination language Lingua Franca (LF). LF has been designed with pragmatics in mind since early on, and some characteristics of LF make it particularly suited for pragmatics-aware programming and modeling. However, the underlying pragmatic principles are broadly applicable, and by now a set of mature open source tools is available for putting them into practice.

LINGUA Franca (lf) is a polyglot coordination language designed for the composition of concurrent, time-sensitive, and potentially distributed reactive components called reactors. The LF coordination layer facilitates the use of target languages (e.g., C, C++, Python, TypeScript) to realize the program logic, where each target language requires a separate runtime implementation that must correctly implement the reactor semantics. Verifying the correctness of runtime implementations is not a trivial task, and is currently done on the basis of regression testing. To provide a more formal verification tool for existing and future target runtimes, as well as to help verify properties of LF programs, we recruit the use of GemocStudio-an Eclipse-based workbench for the development, integration, and use of heterogeneous executable modeling languages. We present an operational model for LF, realized in GEmocStudio, that is primed to interact with a rich set of analysis and verification tools. Our instrumentation provides the ability to navigate the execution of LF programs using an omniscient debugger with graphical model animation; to check assertions in particular execution runs, or exhaustively, using a model checker; and to validate or debug traces obtained from arbitrary LF runtime environments.

Many programming languages and programming frameworks focus on parallel and distributed computing. Several frameworks are based on actors, which provide a more disciplined model for concurrency than threads. The interactions between actors, however, if not constrained, admit nondeterminism. As a consequence, actor programs may exhibit unintended behaviors and are less amenable to rigorous testing. We show that nondeterminism can be handled in a number of ways, surveying dataflow dialects, process networks, synchronous-reactive models, and discrete-event models. These existing approaches, however, tend to require centralized control, pose challenges to modular system design, or introduce a single point of failure. We describe “reactors,” a new coordination model that combines ideas from several of these approaches to enable determinism while preserving much of the style of actors. Reactors promote modularity and allow for distributed execution. By using a logical model of time that can be associated with physical time, reactors also provide control over timing. Reactors also expose parallelism that can be exploited on multicore machines and in distributed configurations without compromising determinacy.

Programmable logic controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This article gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security.

AUTOSAR Adaptive Platform (AP) is an emerging industry standard that tackles the challenges of modern auto-motive software design, but does not provide adequate mechanisms to enforce deterministic execution. This poses profound challenges to testing and maintenance of the application software, which is particularly problematic for safety-critical applications. In this paper, we analyze the problem of nondeterminism in AP and propose a framework for the design of deterministic automotive software that transparently integrates with the AP communication mechanisms. We illustrate our approach in a case study based on the brake assistant demonstrator application that is provided by the AUTOSAR consortium. We show that the original implementation is nondeterministic and discuss a deterministic solution based on our framework.

We discuss a novel approach for constructing deterministic reactive systems that evolves around a temporal model which incorporates a multiplicity of timelines. This model is central to LINGUA FRANCA (LF), a polyglot coordination language and compiler toolchain we are developing for the definition and composition of concurrent components called Reactors, which are objects that react to and emit discrete events. What sets LF apart from other languages that treat time as a first-class citizen is that it confronts the issue that in any reactive system there are at least two distinct timelines involved; a logical one and a physical one-and possibly multiple of each kind. LF provides a mechanism for relating events across timelines, and guarantees deterministic program behavior under quantifiable assumptions.

Programming time-critical systems is notoriously difficult. In this paper we propose an actor-oriented programming model with a semantic notion of time and a deterministic coordination semantics based on discrete events to exercise precise control over both the computational and timing aspects of the system behavior.

Actors have become widespread in programming languages and programming frameworks focused on parallel and distributed computing. While actors provide a more disciplined model for concurrency than threads, their interactions, if not constrained, admit nondeterminism. As a consequence, actor programs may exhibit unintended behaviors and are less amenable to rigorous testing. We show that nondeterminism can be handled in a number of ways, surveying dataflow dialects, process networks, synchronous-reactive models, and discrete-event models. These existing approaches, however, tend to require centralized control, pose challenges to modular system design, or introduce a single point of failure. We describe “reactors,” a new coordination model that combines ideas from several of the aforementioned approaches to enable determinism while preserving much of the style of actors. Reactors promote modularity and allow for distributed execution. By using a logical model of time that can be associated with physical time, reactors also admit control over timing.

This paper describes an implementation in progress of a C-based framework for execution of deterministic, concurrent, real-time software components called "reactors." The component interfaces and their interconnections are given a coordination language called Lingua Franca, while the work done by the components is given in ordinary C. The implementation described here can exploit multiple cores and is capable of realizing rate monotonic and earliest deadline first scheduling policies.

Model-based design methodologies are commonly used in industry for the development of complex cyber-physical systems (CPSs). There are many different languages, tools, and formalisms for model-based design, each with its strengths and weaknesses. Instead of accepting some weaknesses of a particular tool, an alternative is to embrace heterogeneity, and to develop tool integration platforms and protocols to leverage the strengths from different environments. A fairly recent attempt in this direction is the functional mock-up interface (FMI) standard that includes support for co-simulation. Although this standard has reached acceptance in industry, it provides only limited support for simulating systems that mix continuous and discrete behavior, which are typical of CPS. This paper identifies the representation of time as a key problem, because the FMI representation does not support well the discrete events that typically occur at the cyber-physical boundary. We analyze alternatives for representing time in hybrid co-simulation and conclude that a superdense model of time using integers only solves many of these problems. We show how an execution engine can pick an adequate time resolution, and how disparities between time representations internal to co-simulated components and the resulting effects of time quantization can be managed. We propose a concrete extension to the FMI standard for supporting hybrid co-simulation that includes integer time, automatic choice of time resolution, and the use of absent signals. We explain how these extensions can be implemented modularly within the frameworks of existing simulation environments.

No abstract available.

In this paper, we describe a component-based software architecture for the Internet of Things in which proxies for Things and services that we call “accessors” interact with one another under a concurrent, time-stamped, discrete-event (DE) semantics. These proxies are analogous to web pages, which proxy a cloud-based service such as a bank, but instead of being designed to interface those services with humans, accessors are designed to interface services and Things with other services and Things. A deterministic DE semantics is combined with a widely used pattern for handling network interactions that we call asynchronous atomic callbacks (AACs). AAC enables many concurrent pending requests to be active at once without blocking and without the treacherous concurrency pitfalls of threads. In effect, our architecture combines AAC with actors where the actor model has been endowed with a temporal semantics. We show how this architecture can leverage the previously reported secure swarm toolkit (SST) to achieve stateof- the-art authentication, authorization, and encryption of interactions across networks.

This paper presents a master algorithm for co-simulation of hybrid systems using the Functional Mock-up Interface (FMI) standard. Our algorithm introduces step revision to achieve an accurate and precise handling of mixtures of continuous-time and discrete-event signals, particularly in the situation where components are unable to accurately extrapolate their input. Step revision provides an efficient means to respect the error bounds of numerical approximation algorithms that operate inside co-simulated FMUs. We first explain the most fundamental issues associated with hybrid co-simulation and analyze them in the framework of FMI. We demonstrate the necessity for step revision to address some of these issues and formally describe a master algorithm that supports it. Finally, we present experimental results obtained through our reference implementation that is part of our publicly available open-source toolchain called FIDE.