As Artificial Intelligence makes its way to edge devices, it becomes necessary to understand how Learning Enabled Components (LECs) will perform in resource-limited environments. A two-part evaluation procedure is developed here using a combination of testing data to evaluate the LECs' performance, including but not limited to time-domain performance, independent of the target system, for assessing the LEC within the context of system it will be deployed. The target system for this evaluation is a BlueROV2 device simulated in Gazebo using the UUV Simulator package that has been configured to function as an autonomous underwater vehicle (AUV) for infrastructure inspection. The task of the vehicle was to detect and track an underwater pipeline using a side-scan sonar. Nine LEC architectures were constructed in Tensorflow, trained, quantized, and deployed on a Google Edge Tensor Processing Unit (TPU) LEC accelerator for evaluation using generated data and deployed as a hardware-in-the-loop simulation using the BlueROV2 model. It was found that the Mean IoU metric in the evaluations that used sonar data from (simulated) pipes that were smaller than the ones used in the training set had the strongest correlation with the LECs ability to continue to track an underwater pipe during the mission simulations. The size of the model did not increase the time required to load a sonar image on the edge device and interpret the sonar images suggesting that neither model size, nor inference time affected the results of the evaluation. The architecture of a given LEC does not guarantee its performance within the context of an AUV executing a pipe inspection task and, therefore, must be evaluated within the context of the full system to properly characterize its performance.

Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving (~ 0.40 m/s) than the original system (using only LECs).

Cyber-Physical Systems (CPS) are used in many applications where they must perform complex tasks with a high degree of autonomy in uncertain environments. Traditional design flows based on domain knowledge and analytical models are often impractical for tasks such as perception, planning in uncertain environments, control with ill-defined objectives, etc. Machine learning based techniques have demonstrated good performance for such difficult tasks, leading to the introduction of Learning-Enabled Components (LEC) in CPS. Model based design techniques have been successful in the development of traditional CPS, and toolchains which apply these techniques to CPS with LECs are being actively developed. As LECs are critically dependent on training and data, one of the key challenges is to build design automation for them. In this paper, we examine the development of an autonomous Unmanned Underwater Vehicle (UUV) using the Assurance-based Learning-enabled Cyber-physical systems (ALC) Toolchain. Each stage of the development cycle is described including architectural modeling, data collection, LEC training, LEC evaluation and verification, and system-level assurance.

Complex real-time software systems require an active fault management capability. While testing, verification and validation schemes and their constant evolution help improve the dependability of these systems, an active fault management strategy is essential to potentially mitigate the unacceptable behaviors at run-time. In our work we have applied the experience gained from the field of Systems Health Management towards component-based software systems. The software components interact via well-defined concurrency patterns and are executed on a real-time component framework built upon ARINC-653 platform services. In this paper, we present the lessons learned in architecting and applying a two-level health management strategy to assemblies of software components.

Distributed real-time and embedded (DRE) systems are a class of real-time systems formed through a composition of predominantly legacy, closed and statically scheduled real-time subsystems, which comprise over-provisioned resources to deal with worst-case failure scenarios. The formation of the system-of-systems leads to a new range of faults that manifest at different granularities for which no statically defined fault tolerance scheme applies. Thus, dynamic and adaptive fault tolerance mechanisms are needed which must execute within the available resources without compromising the safety and timeliness of existing real-time tasks in the individual subsystems. To address these requirements, this paper describes a middleware solution called Safe Middleware Adaptation for Real-Time Fault Tolerance (SafeMAT), which opportunistically leverages the available slack in the over-provisioned resources of individual subsystems. SafeMAT comprises three primary artifacts: (1) a flexible and configurable distributed, runtime resource monitoring framework that can pinpoint in real-time the available slack in the system that is used in making dynamic and adaptive fault tolerance decisions, (2) a safe and resource aware dynamic failure adaptation algorithm that enables efficient recovery from different granularities of failures within the available slack in the execution schedule while ensuring real-time constraints are not violated and resources are not overloaded, and (3) a framework that empirically validates the correctness of the dynamic mechanisms and the safety of the DRE system. Experimental results evaluating SafeMAT on an avionics application indicates that SafeMAT incurs only 9-15% runtime fail over and 2-6% processor utilization overheads thereby providing safe and predictable failure adaptability in real-time.

SUMMARY The size and complexity of software in safety‐critical systems is increasing at a rapid pace. One technology that can be used to mitigate this complexity is component‐based software development. However, in spite of the apparent benefits of a component‐based approach to development, little work has been done in applying these concepts to hard real‐time systems. This paper improves the state of the art by making three contributions: (1) we present a component model for hard real‐time systems and define the semantics of different types of component interactions; (2) we present an implementation of a middleware that supports this component model. This middleware combines an open‐source CORBA Component Model (CCM) implementation (MICO) with ARINC‐653: a state‐of‐the‐art real‐time operating systems (RTOS) standard, (3) finally; we describe a modeling environment that enables design, analysis, and deployment of component assemblies. We conclude with a discussion of the lessons learned during this exercise. Our experiences point toward extending both the CCM as well as revising the ARINC‐653. Copyright © 2011 John Wiley & Sons, Ltd.

The complexity of software in systems like aerospace vehicles has reached the point where new techniques are needed to ensure system dependability while improving the productivity of developers. One possible approach is to use precisely defined software execution platforms that (1) enable the system to be composed from separate components, (2) restrict component interactions and prevent fault propagation, and (3) whose compositional properties are well-known. In this paper we describe the initial steps towards building a platform that combines component-based software construction with hard real-time operating system services. Specifically, the paper discusses how the CORBA Component Model (CCM) could be combined with the ARINC-653 platform services and the lessons learned from this experiment. The results point towards both extending the CCM as well as revising the ARINC-653.

In this paper we present a model-based approach for designing quality of service adaptive applications. We have developed a prototype distributed QoS modeling environment (DQME) that captures important elements of dynamic QoS adaptation at the model level. This modeling environment is designed independent of and can be integrated with, specific application domains to capture their QoS features and adaptation strategies. It combines the domain-specific modeling capability of the generic modeling environment with the QoS adaptation mechanisms of the quality objects middleware framework. DQME captures both the QoS and the functional concerns of distributed real-time embedded systems, and provides clear separation of these two. Integrated code-synthesis tools facilitate code generation and model refinement. We present a signal analyzer case study to demonstrate the use of the DQME modeling tool in real world applications.