Periodic Tasks Research Articles

Energy efficient dynamic scheduling of dependent tasks for multi‐core real‐time systems using delay techniques

SummaryOptimizing energy consumption and maximizing throughput in multi‐core real‐time architectures through dynamic task scheduling is a critical design challenge. While significant attention has been devoted to addressing this challenge in the domain of real‐time multi‐core scheduling, the focus has primarily centered on considering periodic tasks as independent. However, the existing literature notably lacks comprehensive study of scheduling methodologies on multi‐core systems that consider dependent tasks, though typical real‐time systems execute tasks that share resources. Earlier studies have predominantly examined scenarios involving random new tasks and task instances (jobs), which are executed in different power levels. Each task (and job) has distinct execution time corresponding to each power level. By considering these parameters (power levels and execution times of jobs), various combinations of energy signatures have been found to attain an optimum system state. Building upon this prior research, our paper extends the scope to encompass task scheduling in multi‐core systems with task dependencies. We introduce a novel approach that categorizes dependent tasks into ASAP (as soon as possible) and ALAP (as late as possible) groups, prioritizing task execution based on task mobility—defined as the disparity between the last cycle the task can be scheduled in and the current cycle. Furthermore, our model demonstrates an approach for efficient scheduling of sporadic and aperiodic tasks within this framework. Through experimental validation using randomized task sets, our results indicate that the proposed model achieves a minimum of 5% reduction in normalized total energy consumption compared to existing methodologies.

Observer based output feedback repetitive learning control of robotic manipulators

AbstractThis work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple.

Work-related musculoskeletal complaints: risk factors and impact on work productivity among university administrative employees

BackgroundWork-related musculoskeletal disorders (WMSDs) are a significant workplace problem leading to loss of productivity and disability. Administrative workers perform computer-based tasks for long periods. Consequently, they are at risk of developing musculoskeletal disorders. The objective of this study was to explore the frequency and risk factors of work-related musculoskeletal complaints and their impact on work productivity among administrative employees of Suez Canal University, Egypt.MethodsThis cross-sectional study was conducted on 300 administrative employees through simple random sampling. Data were collected by an interview questionnaire including sociodemographic, work-related data, ergonomic and psychological risk factors, the Nordic Musculoskeletal Questionnaire (NMQ), and the World Health Organization Health and Work Performance Questionnaire (HPQ).ResultsThe frequency of work-related musculoskeletal complaints in at least one anatomical region over the past year was 74.7%. Neck (47.1%), lower back (40.7%), and shoulder (36.3%) were the most reported sites of complaints. Risk factors significantly associated with work-related musculoskeletal complaints were gender, age, physical activity, work experience, workplace stress, sustained body position, awkward posture, and inadequate rest breaks. Logistic regression revealed that older age (OR = 1.039, p = 0.023), being female (OR = 2.175, p = 0.011), and not having adequate rest breaks (OR = 1.979, p = 0.019) were significant predictors for the occurrence of WMSDs. The risk factors of absenteeism include gender, age, marital status, educational level, physical activity, BMI, work experience, and musculoskeletal complaints.ConclusionMusculoskeletal problems were highly prevalent among administrative employees. Being female and not having adequate rest breaks were significant predictors for the occurrence of WMSDs. Ergonomic interventions and improvement of working conditions are recommended to reduce WMSDs.

Co-designing versatile quadruped robots for dynamic and energy-efficient motions

Abstract This paper presents a concurrent optimization approach for the design and motion of a quadruped in order to achieve energy-efficient cyclic behaviors. Computational techniques are applied to improve the development of a novel quadruped prototype. The scale of the robot and its actuators are optimized for energy efficiency considering the complete actuator model including friction, torque, and bandwidth limitations. This method and the optimal bounding trajectories are tested on the first (non-optimized) prototype design iteration showing that our formulation produces a trajectory that (i) can be easily replayed on the real robot and (ii) reduces the power consumption w.r.t. hand-tuned motion heuristics. Power consumption is then optimized for several periodic tasks with co-design. Our results include, but are not limited to, a bounding and backflip task. It appears that, for jumping forward, robots with longer thighs perform better, while, for backflips, longer shanks are better suited. To explore the tradeoff between these different designs, a Pareto set is constructed to guide the next iteration of the prototype. On this set, we find a new design, which will be produced in future work, showing an improvement of at least 52% for each separate task.

Optimization of a task schedule for teams with members having various skills

We consider the real-life problem of planning tasks for teams in a corporation, in conditions of some restrictions. The problem takes into account various constraints, such as for instance flexible working hours, common meeting periods, time set aside for self-learning, lunchtimes and periodic performance of tasks. Additionally, only a part of the team may participate in meetings, and each team member may have their own periodic tasks such as self-development. We propose an algorithm that is an extension of the algorithm dedicated for scheduling on parallel unrelated processors with the makespan criterion. Our approach assumes that each task can be defined by a subset of employees or an entire team. However, each worker is of a different efficiency, so task completion times may differ. Moreover, the tasks are prioritized. The problem is NP-hard. Numerical experiments cover benchmarks with 10 instances of 100 tasks assigned to a 5-person team. For all instances, various algorithms such as branch-and-bound, genetic and tabu search have been tested.

Sequential control barrier functions for mobile robots with dynamic temporal logic specifications

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots.

Energy-Constrained Scheduling for Weakly Hard Real-Time Systems Using Standby-Sparing

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state-of-the-art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint.

A Worst-Case Latency and Age Analysis of Coded Distributed Computing With Unreliable Workers and Periodic Tasks

A Worst-Case Latency and Age Analysis of Coded Distributed Computing With Unreliable Workers and Periodic Tasks

Therapy effect on hand function after home use of a wearable assistive soft-robotic glove supporting grip strength.

Soft-robotic gloves with an assist-as-needed control have the ability to assist daily activities where needed, while stimulating active and highly functional movements within the user's possibilities. Employment of hand activities with glove support might act as training for unsupported hand function. To evaluate the therapeutic effect of a grip-supporting soft-robotic glove as an assistive device at home during daily activities. This multicentre intervention trial consisted of 3 pre-assessments (averaged if steady state = PRE), one post-assessment (POST), and one follow-up assessment (FU). Participants with chronic hand function limitations were included. Participants used the Carbonhand glove during six weeks in their home environment on their most affected hand. They were free to choose which activities to use the glove with and for how long. The primary outcome measure was grip strength, secondary outcome measures were pinch strength, hand function and glove use time. 63 patients with limitations in hand function resulting from various disorders were included. Significant improvements (difference PRE-POST) were found for grip strength (+1.9 kg, CI 0.8 to 3.1; p = 0.002) and hand function, as measured by Jebson-Taylor Hand Function Test (-7.7 s, CI -13.4 to -1.9; p = 0.002) and Action Research Arm Test (+1.0 point, IQR 2.0; p≤0.001). Improvements persisted at FU. Pinch strength improved slightly in all fingers over six-week glove use, however these differences didn't achieve significance. Participants used the soft-robotic glove for a total average of 33.0 hours (SD 35.3), equivalent to 330 min/week (SD 354) or 47 min/day (SD 51). No serious adverse events occurred. The present findings showed that six weeks use of a grip-supporting soft-robotic glove as an assistive device at home resulted in a therapeutic effect on unsupported grip strength and hand function. The glove use time also showed that this wearable, lightweight glove was able to assist participants with the performance of daily tasks for prolonged periods.

Energy management of fault-tolerant real-time embedded systems through switching-activity-based techniques

Energy management and fault tolerance are essential aspects in designing most real-time embedded systems, and dynamic voltage and frequency scaling (DVFS) is a fundamental approach in energy management. Using DVFS can lead to a considerable decrease in the system's reliability as the rate of transient faults increases due to low voltage and frequency. This study focuses on finding and implementing methods that address the following three issues in hard real-time embedded systems: energy consumption, fault tolerance, and time limitations. Switching activity depends on the nature of tasks. Since the switching activity is task-specific, the energy consumption of a task can be significantly greater than that of other tasks. As far as we know, previous studies on the power management of fault-tolerant real-time systems have not considered switching activity, so the impact of this factor is considered in this study. Multiple heuristic methods are proposed based on the switching activity factor to minimize energy consumption while sustaining the system's original reliability. In the proposed methods, the order of task execution is determined based on task switching activities. The methods are developed for both periodic and frame-based task models. The results show that the proposed methods can decrease the energy consumption by 12.7 %∼35.5 % for the periodic task model and 17.4 %∼45.2 % for the frame-based task model while preserving the system's reliability.

Minimizing Energy Consumption of Elastic Robots in Repetitive Tasks

Energy consumption is an important issue in robotics. This article deals with the problem of reducing the energy consumption of compliant electro-mechanical systems while performing periodic tasks. After deriving performance indices to quantify the energy consumption of a mechanical system, we propose a method to determine both the optimal compliant actuation parameters, and link trajectories to minimize energy consumption. We show how this problem can be cast in a simpler one where the optimization regards only parameters that define the shape of periodic trajectories to be subsequently determined by using numerical optimization tools. Indeed, in our framework, the optimal stiffness and spring preload can be analytically obtained as a function of the desired link trajectories. We then provide simulations and experimental validations of the obtained results on a two-link compliant manipulator platform which performs a repetitive pick-and-place task. Our experiments show that the use of compliant actuators instead of rigid ones and the optimization of their compliant parameters give rise to an energy saving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$62\%$</tex-math> </inline-formula> with respect to rigid actuation. Moreover, the simultaneous optimization of the compliant parameters and link trajectories provide an additional energy saving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%$</tex-math> </inline-formula> .

CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning

Small devices are frequently used in IoT and smart-city applications to perform periodic dedicated tasks with soft deadlines. This work focuses on developing methods to derive efficient power-management methods for periodic tasks on small devices. We first study the limitations of the existing Linux built-in methods used in small devices. We illustrate three typical workload/system patterns that are challenging to manage with Linux’s built-in solutions. We develop a reinforcement-learning-based technique with temporal encoding to derive an effective DVFS governor even with the presence of the three system patterns. The derived governor uses only one performance counter, the same as the built-in Linux mechanism, and does not require an explicit task model for the workload. We implemented a prototype system on the Nvidia Jetson Nano Board and experimented with it with six applications, including two self-designed and four benchmark applications. Under different deadline constraints, our approach can quickly derive a DVFS governor that can adapt to performance requirements and outperform the built-in Linux approach in energy saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4 s, the proposed method can save 3%–11% more energy compared to Ondemand. AudioReg and FaceReg applications tested have 5%–14% energy-saving improvement. We have open-sourced the implementation of our in-kernel quantized neural network engine. The codebase can be found at: https://github.com/coladog/tinyagent.

Compositional Timing Analysis of Asynchronized Distributed Cause-effect Chains

Real-time systems require the formal guarantee of timing constraints, not only for the individual tasks but also for the end-to-end latency of data flows. The data flow among multiple tasks, e.g., from sensors to actuators, is described by a cause-effect chain, independent from the priority order of the tasks. In this article, we provide an end-to-end timing-analysis for cause-effect chains on asynchronized distributed systems with periodic task activations, considering the maximum reaction time (MRT) (i.e., the duration of data processing) and the maximum data age (MDA) (i.e., the worst-case data freshness). We first provide an analysis of the end-to-end latency on one local electronic control unit (ECU) that has to consider only the jobs in a bounded time interval. We extend our analysis to globally asynchronized systems by exploiting a compositional property to combine the local results. Throughout synthesized data based on an automotive benchmark as well as on randomized parameters, we show that our analytical results improve the state-of-the-art.

Discovering efficient periodic behaviors in mechanical systems via neural approximators

AbstractIt is well known that conservative mechanical systems exhibit local oscillatory behaviors due to their elastic and gravitational potentials, which completely characterize these periodic motions together with the inertial properties of the system. The classification of these periodic behaviors and their geometric characterization are in an ongoing secular debate, which recently led to the so‐called eigenmanifold theory. The eigenmanifold characterizes nonlinear oscillations as a generalization of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed‐loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient‐descent methods involving neural networks. Extensive simulations show the validity of the approach.

Multi-agent planning and coordination for automated aircraft ground handling

Inspired by the vision of fully autonomous airside operations at Schiphol airport, this study aims to contribute to the short-term goal of automated aircraft ground handling. In this research, we design and evaluate a multi-agent system for planning of automated ground handling. There are two main components in the system: task allocation optimization and multi-agent path planning. To allocate tasks to ground support equipment (GSE) vehicles, an auction mechanism inspired by temporal sequential single item (TeSSI) auction is proposed. Ground handling tasks scheduling for GSE vehicles is modeled as several single-vehicle pickup and delivery optimization problems (SPDP), and the values of the objective functions are used to generate bids for GSE vehicle agents in the auction. Prioritized safe interval path planning for large agents (LA-SIPP) is used to plan collision-free paths for GSE vehicle agents in the model to execute tasks. The aim is to increase the success rates of allocating tasks and finding collision free paths without causing flight delays, given the limited resources such as a small number of available GSE vehicles, time windows constraints and conflicting interests of different agents. Due to the results, even for the instances with frequent flights and the most limited resources, the success rates of allocation and path planning were higher than 81% and 98%, respectively. Furthermore, periodic task allocation and path planning of the ground handling tasks for flights in three aircraft stands during a planning time window of the day, as well as replanning in case of disruptions were performed in a short CPU time. There is a lack of research dealing with the complete process of ground handling, since existing studies concerning the automation of ground handling operations involve fleet assignment or task scheduling models without an integration of detailed path planning. Our main contribution is to present a framework that combines task allocation and path planning for automation of ground handling operations and provides solutions using a multi-agent perspective.

Distributed Multirobot Task Assignment via Consensus ADMM

In this article, we present a distributed algorithm to solve a class of multirobot task assignment problems. We formulate task assignment as a mathematical optimization and solve for optimal solutions with a variant of the consensus alternating direction method of multipliers (C-ADMM). We provide C-ADMM-based algorithms for both the primal and dual problem formulations and show the advantages of each form depending on the problem specifics. In our algorithm, each robot solves a series of local optimization problems and communicates the results to its local neighbors, ultimately converging to an optimal task assignment in problems with linear objective functions and an optimal solution of the relaxed problem in convex problems. While many other distributed algorithms require a central station for their implementation, in our algorithm, each robot only communicates with its one-hop neighbors. In linear task assignment problems, our algorithm converges to the optimal task assignment, unlike many other distributed algorithms for this problem, which yield suboptimal solutions. We demonstrate our algorithms in task assignment problems over a variety of communication network topologies, where we show that our inexact dual algorithm is at least 60% faster than other distributed algorithms, which produce an optimal task assignment. In addition, our dual algorithm attains a 69% speedup in computation time compared to a notable distributed variant of the Hungarian method (Chopra et al., 2017). We also apply our algorithm to a multi-unmanned-aerial-vehicle persistent surveillance problem, showing its suitability for problems involving periodic task assignments.

A fault-tolerant resource locking protocol for multiprocessor real-time systems

This paper presents the first fault-tolerant resource locking protocol for multiprocessor real-time systems. Resource locking protocols control access to shared resources in real-time systems. Most of the previous studies in resource locking protocols have focused on decreasing blocking time and resource conflicts. The use of such protocols in multiprocessor safety-critical real-time systems necessitates fault tolerance in the design of these systems. However, in research focused on multiprocessor real-time systems, fault tolerance has received low attention in resource allocation studies. In addition, many solutions proposed to improve the reliability in real-time scheduling ignored resource models in their task model. This paper proposes a novel fault-tolerant resource locking protocol, called FTPIA-NPCS, which considers transient faults in shared resources. FTPIA-NPCS proposes a checkpointing-based fault-tolerant mechanism to recover from errors caused by transient faults. In this mechanism, a checkpoint is taken from the tasks, before each resource allocation. In addition to providing fault tolerance, FTPIA-NPCS solves the problem of priority inversion in NPCS protocol for periodic tasks with hard deadlines. The simulation results demonstrate that FTPIA-NPCS tolerates at least one transient fault in resources for each execution of the periodic tasks with 15% blocking time overhead in comparison to its non-fault-tolerant configuration, called PIA-NPCS. Furthermore, the blocking time of higher-priority tasks is minimized in comparison to PIA-NPCS. An analytical evaluation of the protocol supports the simulation results.

A New FPGA-Based Task Scheduler for Real-Time Systems

This research demonstrates a novel design of an FPGA-implemented task scheduler for real-time systems that supports both aperiodic and periodic tasks. The periodic tasks are automatically restarted once their period has expired without any need for software intervention. The proposed scheduler utilizes the Earliest-Deadline First (EDF) algorithm and is optimized for multi-core CPUs, capable of executing up to four threads simultaneously. The scheduler also provides support for task suspension, resumption, and enabling inter-task synchronization. The design is based on priority queues, which play a crucial role in decision making and time management. Thanks to the hardware acceleration of the scheduler and the hardware implementation of priority queues, it operates in only two clock cycles, regardless of the number of tasks in the system. The results of the FPGA synthesis, performed on an Intel FPGA device (Cyclone V family), are presented in the paper. The proposed solution was validated through a simplified version of the Universal Verification Methodology (UVM) with millions of test instructions and random deadline and period values.

Feasibility Study for a Python-Based Embedded Real-Time Control System

Because of its simplicity and the support of numerous useful libraries, Python has become one of the most popular programming languages for application development, even in embedded systems. However, in existing control systems where specific tasks must meet specific temporal deadlines and support schedulability with proper priority assignments, the Python interpreter may not satisfy real-time requirements, owing to features such as the global interpreter lock and garbage collector. This paper addresses these constraints with an approach that executes periodic real-time tasks under the fixed-priority preemptible scheduler of RT-Preempt. First, we implemented a Python real-time module that allows users to create and execute periodic tasks with fixed priorities based on Python. Then, we conducted experiments on an open embedded system, in this case, a Raspberry Pi 4. We evaluated the real-time performance, focusing on test metrics for control systems, such as task periodicity, responsiveness, and interrupt response. The results were then compared to those of conventional real-time tasks developed using the C language to validate the feasibility of the proposed method. Finally, we performed experimental validation by tracking the position of EtherCAT servo motors to demonstrate the feasibility of a Python-based real-time control system in a practical application.

Frequency Response Function-Based Learning Control: Analysis and Design for Finite-Time Convergence

Iterative learning control (ILC) enables substantial performance improvement by using past operational data in combination with approximate plant models. The aim of this article is to develop an ILC framework based on nonparametric frequency response function (FRF) models that requires very limited modeling effort. These FRF models describe the behavior of a system in periodic steady state, yet are employed for the control of arbitrary finite-length tasks. A detailed analysis and design framework is developed to construct noncausal learning filters directly from uncertain FRF models, that achieve ILC convergence for arbitrary tasks. The resulting framework provides a unification between ILC and iterative inversion-based control, where the latter is a learning method specifically developed for periodic tasks.