Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. So far, however, feedback control over wireless multi-hop networks has only been shown for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over wireless multi-hop networks for update intervals of 20 to 50 milliseconds.Comment: Accepted final version to appear in: 10th ACM/IEEE International Conference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS '19), April 16--18, 2019, Montreal, QC, Canad

Demo Abstract: Contract-based Hierarchical Resilience Framework for Cyber-Physical Systems

This demonstration presents a framework for building a resilient Cyber-Physical Systems (CPS) cyber-infrastructure through the use of hierarchical parametric assume-guarantee contracts. A Fischertechnik Sorting Line with Color Detection training model is used to showcase our framework.Comment: 2 pages, 5 figures, published in the Demo Session of IEEE International Conference on Cyber-Physical Systems 2019. Publication rights licensed to AC

Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems

This abstract describes the first public demonstration of feedback control and coordination of multiple physical systems over a dynamic multi-hop low-power wireless network with update intervals of tens of milliseconds. Our running system can dynamically change between different sets of application tasks (e.g., sensing, actuation, control) executing on the spatially distributed embedded devices, while closed-loop stability is provably guaranteed even across those so-called mode changes. Moreover, any subset of the devices can move freely, which does not affect closed-loop stability and control performance as long as the wireless network remains connected.Comment: Proceedings of the 18th International Conference on Information Processing in Sensor Networks (IPSN'19), April 16--18, 2019, Montreal, QC, Canad

Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant benefits brought by ML techniques, they also raise additional safety issues because 1) most expressive and powerful ML models are not transparent and behave as a black box and 2) the training data which plays a crucial role in ML safety is usually incomplete. An important technique to achieve safety for ML models is "Safe Fail", i.e., a model selects a reject option and applies the backup solution, a traditional controller or a human operator for example, when it has low confidence in a prediction. Data-driven models produced by ML algorithms learn from training data, and hence they are only as good as the examples they have learnt. As pointed in [17], ML models work well in the "training space" (i.e., feature space with sufficient training data), but they could not extrapolate beyond the training space. As observed in many previous studies, a feature space that lacks training data generally has a much higher error rate than the one that contains sufficient training samples [31]. Therefore, it is essential to identify the training space and avoid extrapolating beyond the training space. In this paper, we propose an efficient Feature Space Partitioning Tree (FSPT) to address this problem. Using experiments, we also show that, a strong relationship exists between model performance and FSPT score.Comment: Publication rights licensed to AC

Does the dataset meet your expectations? Explaining sample representation in image data

Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When a dataset is labeled,we n ote that annotations alone are capable of providing a human interpretable summary of sample diversity. This allows explaining any lack of diversity as the mismatch found when comparing the actual distribution of annotations in the dataset with an expected distribution of annotations, specified manually to capture essential label diversity. While, in many practical cases, labeling (samples → annotations) is expensive, its inverse, simulation (annotations → samples) can be cheaper. By mapping the expected distribution of annotations into test samples using parametric simulation, we present a method that explains sample representation using the mismatch in diversity between simulated and collected data.We then apply the method to examine a dataset of geometric shapes to qualitatively and quantitatively explain sample representation in termsof comprehensible aspects such as size, position, and pixel brightness

Does the dataset meet your expectations? Explaining sample representation in image data

Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When a dataset is labeled,we n ote that annotations alone are capable of providing a human interpretable summary of sample diversity. This allows explaining any lack of diversity as the mismatch found when comparing the actual distribution of annotations in the dataset with an expected distribution of annotations, specified manually to capture essential label diversity. While, in many practical cases, labeling (samples → annotations) is expensive, its inverse, simulation (annotations → samples) can be cheaper. By mapping the expected distribution of annotations into test samples using parametric simulation, we present a method that explains sample representation using the mismatch in diversity between simulated and collected data.We then apply the method to examine a dataset of geometric shapes to qualitatively and quantitatively explain sample representation in termsof comprehensible aspects such as size, position, and pixel brightness

Multiparty motion coordination: from choreographies to robotics programs

We present a programming model and typing discipline for complex multi-robot coordination programming. Our model encompasses both synchronisation through message passing and continuous-time dynamic motion primitives in physical space. We specify continuous-time motion primitives in an assume-guarantee logic that ensures compatibility of motion primitives as well as collision freedom. We specify global behaviour of programs in a choreographic type system that extends multiparty session types with jointly executed motion primitives, predicated refinements, as well as a separating conjunction that allows reasoning about subsets of interacting robots. We describe a notion of well-formedness for global types that ensures motion and communication can be correctly synchronised and provide algorithms for checking well-formedness, projecting a type, and local type checking. A well-typed program is communication safe, motion compatible, and collision free. Our type system provides a compositional approach to ensuring these properties. We have implemented our model on top of the ROS framework. This allows us to program multi-robot coordination scenarios on top of commercial and custom robotics hardware platforms. We show through case studies that we can model and statically verify quite complex manoeuvres involving multiple manipulators and mobile robots---such examples are beyond the scope of previous approaches

Motion session types for robotic interactions

Robotics applications involve programming concurrent components synchronising through messages while simultaneously executing motion primitives that control the state of the physical world. Today, these applications are typically programmed in low-level imperative programming languages which provide little support for abstraction or reasoning. We present a unifying programming model for concurrent message-passing systems that additionally control the evolution of physical state variables, together with a compositional reasoning framework based on multiparty session types. Our programming model combines message-passing concurrent processes with motion primitives. Processes represent autonomous components in a robotic assembly, such as a cart or a robotic arm, and they synchronise via discrete messages as well as via motion primitives. Continuous evolution of trajectories under the action of controllers is also modelled by motion primitives, which operate in global, physical time. We use multiparty session types as specifications to orchestrate discrete message-passing concurrency and continuous flow of trajectories. A global session type specifies the communication protocol among the components with joint motion primitives. A projection from a global type ensures that jointly executed actions at end-points are communication safe and deadlock-free, i.e., session-typed components do not get stuck. Together, these checks provide a compositional verification methodology for assemblies of robotic components with respect to concurrency invariants such as a progress property of communications as well as dynamic invariants such as absence of collision. We have implemented our core language and, through initial experiments, have shown how multiparty session types can be used to specify and compositionally verify robotic systems implemented on top of off-the-shelf and custom hardware using standard robotics application libraries

Data-Driven Virtual Flow Rate Sensor Development for Leakage Monitoring at the Cradle Bearing in an Axial Piston Pump

The leakage of the tribological contact in axial piston pumps significantly impacts the pump efficiency. Leakage observations can be used to optimize the pump design and monitor the behavior of the tribological contact. However, due to assembly limitations, it is not always feasible to observe the leakage of each tribological contact individually with a flow rate sensor. This work developed a data-driven virtual flow rate sensor for monitoring the leakage of cradle bearings in axial piston pumps under different operating conditions and recess pressures. The performance of neural network, support vector regression, and Gaussian regression methods for developing the virtual flow rate sensor was systematically investigated. In addition, the effect of the number of datasets and label distribution on the performance of the virtual flow sensor were systematically studied. The findings are verified using a data-driven virtual flow rate sensor to observe the leakage. In addition, they show that the distribution of labels significantly impacts the model’s performance when using support vector regression and Gaussian regression. Neural network is relatively robust to the distribution of labeled data. Moreover, the datasets also influence model performance but are not as significant as the label distribution