Shared Control Methods Research Articles

Human-Machine Shared Steering Control for Vehicle Lane Keeping Systems via a Fuzzy Observer-Based Event-Triggered Method

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

A data-driven model-based shared control strategy considering drivers’ adaptive behavior in driver-automation interaction

Shared control scheme improves the driving performance while having an impact on driver behavior, drivers would constantly adapt their steering behavior mechanism in interaction with a shared controller. This paper proposes a novel data-driven model-based shared control strategy which is capable of considering drivers’ adaptive behaviors in driver-automation interaction to improve safety. The Koopman operator theory, which is a pure data-driven modeling technology, is adopted to yield an explicit control-oriented driver-vehicle model for shared controller design. Besides, a weighted online extended dynamic mode decomposition (WOEDMD) algorithm is proposed to update the Koopman driver model online for better capturing the driver’s adaptive behavior in driver-automation interaction, which settles the problem of driver’s potential behavior mechanism variations in practice. Based on the Koopman driver-vehicle model, a model-based shared controller is proposed in the model predictive control (MPC) framework, and the potential fields are incorporated in the optimization objectives to ensure safety. A group of human-in-the-loop experiments are conducted on a driving simulator to demonstrate the effectiveness of the modeling and shared control methods. The results show that the Koopman operator theory can be exploited for modeling the dynamics of the driver-vehicle integrated system, and the drivers’ adaptive behavior can be captured by the WOEDMD algorithm. Moreover, the shared controller considering the driver’s adaptive behavior improves the driving safety in the collision avoidance task.

A brain-actuated robotic arm system using non-invasive hybrid brain–computer interface and shared control strategy

Objective. The electroencephalography (EEG)-based brain–computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer’s experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects. Approach. Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user’s intention and generate automatic commands. Based on the inference confidence and user’s characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm. Main results. Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately. Significance. In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.

Toward Seamless Transitions Between Shared Control and Supervised Autonomy in Robotic Assistance

Assistive robots aim to help humans with impairments execute motor tasks in everyday household environments. Controlling the end-effector of such robots directly, for instance with a joystick, is often cumbersome. Shared control methods, like Shared Control Templates (SCTs) [1] , have therefore been proposed to provide support for robotic control. Moreover, depending on factors such as workload, system trust or engagement, users may like to freely adjust the level of autonomy, for instance by letting the robot complete a task by itself. In this letter, we present a concept for adjustable autonomy in the context of robotic assistance. We extend the SCT approach with an automatic control module that allows the user to switch between Shared Control and Supervised Autonomy at any time during task execution. As both support modes use the same action representation, transitions are seamless. We show the capabilities of this approach in a set of daily living tasks with our wheelchair-mounted robot EDAN and our humanoid robot Rollin’ Justin. We highlight how automatic execution benefits from SCT features, like task-related constraints and whole-body control.

Human-machine shared control: New avenue to dexterous prosthetic hand manipulation

At present, albeit the dexterous hand prostheses of multiple degrees of freedom (DOFs) have become prosperous on the market, the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively low. The unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous products. Recently, driven by the wave of artificial intelligence (AI), a series of shared control methods have emerged, in which “bodily function” (myoelectric control) and “artificial intelligence” (local autonomy, computer vision, etc.) are tightly integrated, and provided a new conceptual solution for the intuitive operation of dexterous prostheses. In this paper, the background and development trends of this type of methods are described in detail, and the potential development directions and the key technologies that need breakthroughs are indicated. In practice, we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control, multi-finger grasp autonomy, and augmented reality (AR) feedback together. This method “divides” the human sophisticated reach-and-grasp task into several subtasks, and then “conquers” them by using different strategies from either human or machine perspective. It is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.

A Teleoperation Framework for Mobile Robots Based on Shared Control

Mobile robots can complete a task in cooperation with a human partner. In this letter, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and electromyography (EMG) signals sensor to control the mobile robot. A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform. This shared control method enables the human partner to tele-control the mobile robot's motion and achieve obstacles avoidance synchronously. Compared with conventional shared control methods, this proposed one provides a force feedback based on muscle activation and drives the human partners to update their control intention with predictability. Experimental results demonstrate the enhanced performance of the mobile robots in comparison with the methods in the literature.

A Review of EEG-Based Brain-Computer Interface Systems Design

A brain-computer interface (BCI) system can recognize the mental activities pattern by computer algorithms to control the external devices. Electroencephalogram (EEG) is one of the most common used approach for BCI due to the convenience and non-invasive implement. Therefore, more and more BCIs have been designed for the disabled people that suffer from stroke or spinal cord injury to help them for rehabilitation and life. We introduce the common BCI paradigms, the signal processing, and feature extraction methods. Then, we survey the different combined modes of hybrids BCIs and review the design of the synchronous/asynchronous BCIs. Finally, the shared control methods are discussed.

Haptic-Based Shared-Control Methods for a Dual-Arm System

We propose novel haptic guidance methods for a dual-arm telerobotic manipulation system, which are able to deal with several different constraints, such as collisions, joint limits, and singularities. We combine the haptic guidance with shared-control algorithms for autonomous orientation control and collision avoidance meant to further simplify the execution of grasping tasks. The stability of the overall system in various control modalities is presented and analyzed via passivity arguments. In addition, a human subject study is carried out to assess the effectiveness and applicability of the proposed control approaches both in simulated and real scenarios. Results show that the proposed haptic-enabled shared-control methods significantly improve the performance of grasping tasks with respect to the use of classic teleoperation with neither haptic guidance nor shared control.

Shared control on lunar spacecraft teleoperation rendezvous operations with large time delay

Teleoperation could be used in space on-orbit serving missions, such as object deorbits, spacecraft approaches, and automatic rendezvous and docking back-up systems. Teleoperation rendezvous and docking in lunar orbit may encounter bottlenecks for the inherent time delay in the communication link and the limited measurement accuracy of sensors. Moreover, human intervention is unsuitable in view of the partial communication coverage problem. To solve these problems, a shared control strategy for teleoperation rendezvous and docking is detailed. The control authority in lunar orbital maneuvers that involves two spacecraft as rendezvous and docking in the final phase was discussed in this paper. The predictive display model based on the relative dynamic equations is established to overcome the influence of the large time delay in communication link. We discuss and attempt to prove via consistent, ground-based simulations the relative merits of fully autonomous control mode (i.e., onboard computer-based), fully manual control (i.e., human-driven at the ground station) and shared control mode. The simulation experiments were conducted on the nine-degrees-of-freedom teleoperation rendezvous and docking simulation platform. Simulation results indicated that the shared control methods can overcome the influence of time delay effects. In addition, the docking success probability of shared control method was enhanced compared with automatic and manual modes.

Simultaneous Achievement of Supporting Human Drivers and Improving Driving Skills by Shared and Cooperative Control

Abstract: Advanced driver assistance systems (ADASs) have successfully reduced drivers’ workload and increased safety. However, the excessive use of such assistance systems might reduce the chances of developing drivers’ skill. Haptic-shared and cooperative controls are methods in which the automated system shares a single operation with human operators and provides partial assistance, respectively. Effective collaboration between a human operator and an automated system can be achieved by the tuning controller, which is introduced to facilitate the improvement of human operators’ skills. We discuss a methodology to decrease drivers’ workload and improve their driving skills by using haptic shared and cooperative control methods. Backward parking assist by haptic shared control is presented to increase performance during assistance and skill improvement after assistance.

Obscuring But Not Reducing Managerial Control: Does TQM Measure up to Democracy Standards?

Despite TQM's (total quality management) stated emphasis on more cooperative employee-management relations, we argue that TQM, as it is presently conceived and practiced, obscures but does not reduce management control over workers. Based upon examination of the foundational TQM texts and of 25 implementation guides, we show how the language of 'empowerment' and 'self-management', central to the TQM mission, is often used in equivocal ways that compromise the meaning of these concepts as they were originally developed and used by radically egalitarian grassroots organizations. We also show in several respects how TQM's organizational changes may further perpetuate inequality among workers. By distilling the key ideal-typical features of the total quality organization and comparing them with the ideal-type characteristics of the machinebased bureaucracy and of the egalitarian-democratic organization, we bring into sharp relief how they are not the same. In bringing to the TQM literature the practices and standards developed over a 150-year history by genuinely egalitarian cooperative organizations, we are able to show the profound differences in form that prevail despite their parallel language. Should TQM practitioners wish to adopt the substance of these shared control methods, they would do well to learn from the examples of the grassroots organizations that pioneered these empowerment methods.