Task-based Control Research Articles

Control and consent regime dynamics within labour platforms

Much is known about how labour platforms use ‘algorithmic management’ to implement rules which govern labour by matching workers (or service providers) with clients (or users). But little is known about whether and how platform workers engage with these rules by manipulating them to their own advantage, and how this accounts for wider ‘regime dynamics’ across (and within) different types of platforms (for example, on-location and online). Based on a comparative analysis of two food delivery (Deliveroo and Takeaway) and two freelancing (Upwork and Jellow) platforms in Belgium, we discuss the rules platforms use to govern labour and examine what role workers have in shaping a ‘space’ of control over the conduct of their work. Drawing on labour process theory, we argue that this space is shaped by the way in which platforms shift risks onto workers by rules governing access to work through rewards, penalties as well as labour deployment reflecting various contractual statuses. Hence, we explain how workers also shape such spaces by organising consent around these rules, pointing to a ‘social space’ for food delivery workers and a ‘market space’ for self-employed freelancers. These spaces refer to different regime types, that is, ‘pay-based control’ and ‘time-based control’ for food delivery, and ‘customer-based control’ and ‘task-based control’ for online freelancers. These types are shaped by the control and consent dynamics within labour platforms, reflecting the platforms’ labour governance strategies and workers’ attempts to ensure control over these strategies within the distinctive political institutional realm.

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.

The FACTS model of speech motor control: Fusing state estimation and task-based control.

We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. FACTS employs a hierarchical state feedback control architecture to control simulated vocal tract and produce intelligible speech. The model includes higher-level control of speech tasks and lower-level control of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, after Task Dynamics. Both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This estimate is derived, based on efference copy of applied controls, from a forward model that predicts both the next vocal tract state as well as expected auditory and somatosensory feedback. A comparison between predicted feedback and actual feedback is then used to update the internal state prediction. FACTS is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

Task-Based Control and Design of a BLDC Actuator for Robotics

This letter proposes a new multi-input brushless dc motor current control policy aimed at robotics applications. The controller achieves empirical improvements in steady-state torque and power-production abilities relative to conventional controllers, while retaining similarly good torque-tracking and stability characteristics. Simulations show that non-conventional motor design optimizations, whose feasibility is established by scaling model extrapolations from existing motor catalogs, can vastly amplify the effectiveness of this new control strategy.

Iteratively Successive Projection: A Novel Continuous Approach for the Task-Based Control of Redundant Robots

The task-based control approach with an activation task mechanism endows a robot the possibility to act complex behaviors in an unstructured environment. However, the task-based control approach based on the classical inverse may suffer from discontinuities as tasks switch between inactive and active states. In this paper, an iteratively successive projection method is proposed to construct a null space projection operator by iteratively successive projecting the space between the null space of each task. And the null space projection operator constructed by the iteratively successive projection method should converge to the null space projection operator obtained by the classical inverse, as the iteration time increases to infinity. By introducing activation factors, a continuous projection operator is obtained with a finite-time iteration. Based on the continuous projection operator, a closed-loop control algorithm is presented, which ensures both the continuity of the joint motion and the boundedness of the tracking error. The simulations on a six-link planar manipulator for the trajectory tracking task in the presence of obstacles show the efficacy of the proposed method in continuity, stability, and calculation-time consumption.

The Underwater Swimming Manipulator—A Bioinspired Solution for Subsea Operations

Underwater vehicles have carried out subsea operations for many decades, and since the 1980s, remotely operated vehicles (ROVs) have been essential for the development and maintenance of subsea installations. As the technology has progressed, various types of vehicles have been developed to perform subsea inspection, maintenance, and repair (IMR) operations, including conventional work class ROVs, inspection class ROVs, autonomous underwater vehicles (AUVs), and, more recently, intervention AUVs. The underwater swimming manipulator (USM), presented in this paper, is an innovative bioinspired addition to the family of underwater robotic vehicles. The overall vision of the USM is to provide a significant impact on how to perform inspection and light intervention tasks. In this paper, we discuss the most important applications for the USM and the main challenges related to modeling, guidance, and control of this innovative vehicle. We provide a detailed description of the concept of the USM, together with a proposed generic motion control framework. A kinematic and dynamic model of the USM is derived for the purpose of designing control algorithms, and selected task-based control approaches are presented, based on inverse kinematic control. We also present the development of a simulation environment, a simulation model of the USM, and provide simulations to support the use of USMs for subsea IMR operations.

Novel Functional Task-Based Gait Assistance Control of Lower Extremity Assistive Device for Level Walking

A novel approach to functional task-based gait assistance control architecture and its preliminary evaluations are presented. This approach utilizes a finite state machine to implement a gait period detector to estimate the current gait period of the user among the six major periods. Furthermore, an impedance-based controller is used to produce the functional gait assistance at the hip and the knee joint in each detected gait period. This is the first experimental trial to offer gait assistance based on gait period functionality. The goal of this controller is to assist the gait of the impaired persons in order to reduce the walking efforts in their daily living. The performance of this approach is investigated on a single-leg lower extremity assistive device with a group of healthy subjects walking on a treadmill. Joint kinematics, assistive torques from assisted trial, and heart rates from all trials are recorded. Preliminary evaluation shows that this approach is capable of providing functional task gait assistance at hip and knee joint of the subject under varying walking speeds during level walking. In particular, at a constant speed of 1 km/h with five healthy subjects, when assisted with the device, it reduces their heart rate to statistically resemble the free-walking heart rates. Additionally, it decreases their physical efforts to substantially improve their performance, compared with their unassisted conditions.

Visual servo walking control for humanoids with finite-time convergence and smooth robot velocities

ABSTRACTIn this paper, we address the problem of humanoid locomotion guided from information of a monocular camera. The goal of the robot is to reach a desired location defined in terms of a target image, i.e., a positioning task. The proposed approach allows us to introduce a desired time to complete the positioning task, which is advantageous in contrast to the classical exponential convergence. In particular, finite-time convergence is achieved while generating smooth robot velocities and considering the omnidirectional waking capability of the robot. In addition, we propose a hierarchical task-based control scheme, which can simultaneously handle the visual positioning and the obstacle avoidance tasks without affecting the desired time of convergence. The controller is able to activate or inactivate the obstacle avoidance task without generating discontinuous velocity references while the humanoid is walking. Stability of the closed loop for the two task-based control is demonstrated theoretically even during the transitions between the tasks. The proposed approach is generic in the sense that different visual control schemes are supported. We evaluate a homography-based visual servoing for position-based and image-based modalities, as well as for eye-in-hand and eye-to-hand configurations. The experimental evaluation is performed with the humanoid robot NAO.

A Single Operator Implementation of Task BasedMultiple Unmanned Vehicle

Automation has greatly reduced operator workload and generally enhanced safety in supervisory control settings. There has recently been a significant amount of activity in developing supervisory control algorithms for multiple unmanned vehicle operation by a single operator. In the both military and commercial sectors there has been significant research and development in the field of unmanned vehicles. The objective of this paper is use to control multiple UV’s using single operator by RESCHU software. RESCHU (Research Environment for Supervisory Control of Homogeneous Unmanned vehicles) which allows a single operator have the ability to control multiple UVs. This paper approaches two methods namely Vehicle-Based control (where a single operator individually assigns tasks to unmanned assets) and Task-Based control (where the operator generates a task list, which is then given to the group of vehicles that determine how to best divide the tasks among themselves). The proposed framework incorporates (i) sensor constraints, such as processing/travel time, and (ii) task constraints, such as prioritization. These results have implications for the design of future human-UV systems, as well as more general multiple task supervisory control models.

A Sensor-Based Dual-Arm Tele-Robotic System

We present a novel system to achieve coordinated task-based control on a dual-arm industrial robot for the general tasks of visual servoing and bimanual hybrid motion/force control. The industrial robot, consisting of a rotating torso and two seven degree-of-freedom arms, performs autonomous vision-based target alignment of both arms with the aid of fiducial markers, two-handed grasping and force control, and robust object manipulation in a tele-robotic framework. The operator uses hand motions to command the desired position for the object via Microsoft Kinect while the autonomous force controller maintains a stable grasp. Gestures detected by the Kinect are also used to dictate different operation modes. We demonstrate the effectiveness of our approach using a variety of common objects with different sizes, shapes, weights, and surface compliances.

Research of Access Control Models in Personal Networks

How to prevent illegal users from sharing system resources was one of the main purposes for MAGNET Security Group. This paper introduced some major access control models such as traditional access control models, role-based access control model (RBAC), task-based access control model (TBAC) and role-task-based access control model (T-RBAC). In the end, a feasible scheme PN_T-RBAC was proposed at the base of the T-RBAC model in existence, which was suitable for the coalition environment of personal networks.

Research on Task-Based Usage Control Core Models in Workflow for Manufacturing Environment

With widespread application of Workflow (WF) management system, problem in access control becomes more and more important. At present, the TBAC Model and TRBAC Model cannot satisfy the requirements of access control in Modern information systems. Therefore, the task in WF is the center which combine UCON model with WF, and establish the Task-Based Usage Control (TUCON) Model in which the access control of WF use the UCON Model.

Intermediate Desired Value Approach for Task Transition of Robots in Kinematic Control

The task-based control framework is well established for its ability to generate complex behavior in versatile robots. When executing multiple complex tasks, continuous and stable transition among these tasks is one of the most important issues. In this paper, the problem of task transition is discussed to achieve continuous transitions between arbitrary tasks effectively. Instead of modifying the control laws, the design of intermediate desired values to be realized by existing controllers is proposed. The proposed approach can deal with arbitrary task sets, with or without priorities, for insertion and removal, and with priority rearrangement for hierarchical sets of tasks. The solution is generic and can be used for any type of transition. Two examples of uses include a time-driven transition to execute a given task schedule and a transition depending on the robot configuration to perform joint-limit avoidance behaviors. The performance of the algorithm is verified in simulations and on a physical robot.

Robust task-based control policies for physics-based characters

We present a method for precomputing robust task-based control policies for physically simulated characters. This allows for characters that can demonstrate skill and purpose in completing a given task, such as walking to a target location, while physically interacting with the environment in significant ways. As input, the method assumes an abstract action vocabulary consisting of balance-aware, step-based controllers. A novel constrained state exploration phase is first used to define a character dynamics model as well as a finite volume of character states over which the control policy will be defined. An optimized control policy is then computed using reinforcement learning. The final policy spans the cross-product of the character state and task state, and is more robust than the conrollers it is constructed from. We demonstrate real-time results for six locomotion-based tasks and on three highly-varied bipedal characters. We further provide a game-scenario demonstration.

Constraints on task-based control of behaviour following frontal lobe damage: A single-case study

What factors determine stimulus-driven responses in patients with utilization behaviour? We examined this question by assessing the influence of an irrelevant cue on visual search in a patient showing evidence of utilization behaviour (F.K.), following bilateral damage to the medial frontal and temporal lobes. Despite being able to repeat the instructions, F.K. often responded to an item in the search display that matched the cue rather than the target. This effect was reduced under certain conditions: (a) when the cue–search interval increased, (b) when F.K. paid less attention to the cue, and (c) when the target discrimination task was made more difficult. On the other hand, the effect arose even when the cue was always invalid. We suggest that information from the cue competed with the top-down set to determine search. F.K.'s lesion makes it difficult for him to impose top-down knowledge rapidly, leading to responses automatically being based on attended, but irrelevant, cues under short cue–display intervals.

Silica dust control in small-scale building/structure demolition operations using good work practice guidance

Work practices can influence exposure, especially in small-scale operations conducted by mobile work crews. This study evaluated the use of information on good work practice in control guidance sheets adapted from UK Silica Essentials guidance sheets by trained workers and supervisors employed in small-scale concrete and masonry demolition operations. A one-page employee silica task-based control guidance sheet for each of four demolition tasks and multiple-page silica control guidance for supervisors were developed. Interactive, hands-on worker training on these task-based good work practice controls was developed. Training was presented to 26 participants from two demolition crews. Feedback on the training and task-based good work practice control guidance sheets was elicited. Observations of work practices were made before and after training. Participants indicated gains in knowledge and checklists were used to document skill attainment. The quality of the training and usefulness of the material/skills was rated high by trainees. Increased use of water to suppress dust and wet cleaning methods on the job were documented following the training. Additional follow-up after training is required to determine long-term impact on sustained changes in work practices, and to evaluate the need for refresher training.

Spatial Motion Constraints Using Virtual Fixtures Generated by Anatomy

This paper describes a spatial-motion-constraints-generation approach for a human-machine collaborative surgical-assistant system from registered computer tomography models. We extend constrained optimization formulation incorporating task goals, anatomy-based constraints, "no fly zones," etc. We use a fast potential-collision-constraint-detection method based on a 3-D surface model and covariance tree data structure. These boundary constraints, along with task behaviors and joint limits, serve as constraint conditions for constrained robot control. We are able to follow a complex path inside a human skull, phantom represented by a surface model composed of 99 000 vertices and 182 000 triangles in real time. Our approach enables real-time task-based control of a surgical robot in a precise interactive minimally invasive surgery task. We illustrate our approach based on two example tasks which are analogous to the procedures in endoscopic sinus surgery, and analyze the user's performance on both teleoperation and cooperative control for one of the example tasks. The experimental results show that a robotic assistant employing our approach on spatial motion constraints can assist the user in skilled manipulation tasks, while maintaining desired properties. Our approach is equally applicable to teleoperative and cooperative controlled robots

Research of user access control for networked manufacturing system

An integrated user access control method was proposed to address the issues of security and management in networked manufacturing systems (NMS). Based on the analysis of the security issues in networked manufacturing system, an integrated user access control method composed of role-based access control (RBAC), task-based access control (TBAC), relationship-driven access control (RDAC) and coalition-based access control (CBAC) was proposed, including the hierarchical user relationship model, the reference model and the process model. The elements and their relationships were defined, and the expressions of constraints authorization were given. The extensible access control markup language (XACML) was used to implement this method. This method was used in the networked manufacturing system in the Shaoxing spinning region of China. The results show that the integrated user access control method can reduce the costs of system security maintenance and management.

Learning temporal structure for task based control

We present an extension for variable length Markov models (VLMMs) to allow for modelling of continuous input data and show that the generative properties of these VLMMs are a powerful tool for dealing with real world tracking issues. We explore methods for addressing the temporal correspondence problem in the context of a practical hand tracker, which is essential to support expectation in task-based control using these behavioural models. The hand tracker forms a part of a larger multi-component distributed system, providing 3-D hand position data to a gesture recogniser client. We show how the performance of such a hand tracker can be improved by using feedback from the gesture recogniser client. In particular, feedback based on the generative extrapolation of the recogniser's internal models is shown to help the tracker deal with mid-term occlusion. We also show that VLMMs can be used as a means to inform the prior in an expectation maximisation (EM) process used for joint spatial and temporal learning of image features.

Interpreting a dynamic and uncertain world: task-based control

In this paper we show that it can be beneficial to have a high-level vision component that guides the reasoning of the whole vision system when interpreting a dynamic and uncertain world. This guidance is provided by an attentional mechanism that exploits knowledge of the specific problem being solved. Here we develop a general framework for such an attentional mechanism and its application to understanding dynamic scenes. This attentional mechanism can enable a vision system to perform a given domain task while expending minimal resources. We have developed a component that uses Bayesian networks combined with a deictic representation to select what, when and how to use processed data from a fixed camera. We apply two forms of Bayesian network, which (1) create a dynamic structure to reflect the spatial organisation of the data and (2) measure task relatedness. Together these give attentional focus making the reasoning performed relevant to the task.