Shared Control
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2021 ◽  
Vol 8 ◽  
Mark Zolotas ◽  
Murphy Wonsick ◽  
Philip Long ◽  
Taşkın Padır

In remote applications that mandate human supervision, shared control can prove vital by establishing a harmonious balance between the high-level cognition of a user and the low-level autonomy of a robot. Though in practice, achieving this balance is a challenging endeavor that largely depends on whether the operator effectively interprets the underlying shared control. Inspired by recent works on using immersive technologies to expose the internal shared control, we develop a virtual reality system to visually guide human-in-the-loop manipulation. Our implementation of shared control teleoperation employs end effector manipulability polytopes, which are geometrical constructs that embed joint limit and environmental constraints. These constructs capture a holistic view of the constrained manipulator’s motion and can thus be visually represented as feedback for users on their operable space of movement. To assess the efficacy of our proposed approach, we consider a teleoperation task where users manipulate a screwdriver attached to a robotic arm’s end effector. A pilot study with prospective operators is first conducted to discern which graphical cues and virtual reality setup are most preferable. Feedback from this study informs the final design of our virtual reality system, which is subsequently evaluated in the actual screwdriver teleoperation experiment. Our experimental findings support the utility of using polytopes for shared control teleoperation, but hint at the need for longer-term studies to garner their full benefits as virtual guides.

2021 ◽  
Yue Liu ◽  
Qing Xu ◽  
Hongyan Guo ◽  

Abstract The driver-automation shared driving is a transition to fully-autonomous driving, in which human driver and vehicular controller cooperatively share the control authority. This paper investigates the shared steering control of semiautonomous vehicles with uncertainty from imprecise parameter. By considering driver’s lane-keeping behaviour on the vehicle system, a driver-automation shared driving model is introduced for control purpose. Based on the interval type-2 (IT2) fuzzy theory, moreover, the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model. After that, the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability. In this work, sufficient design conditions in terms of linear matrix inequalities are derived, to guarantee the closed-loop stability of the driver-automation shared control system. Meanwhile, an H ∞ performance is studied to ensure the robustness of the control system. Finally, simulationbased results are provided to demonstrate the performance of the proposed control method. Furthermore, an existing type-1 fuzzy controller is introduced as comparison, to verify the superiority of the proposed IT2 fuzzy controller.

2021 ◽  
Vol 11 (16) ◽  
pp. 7340
Dana Gutman ◽  
Samuel Olatunji ◽  
Yael Edan

This study explored how levels of automation (LOA) influence human robot collaboration when operating at different levels of workload. Two LOA modes were designed, implemented, and evaluated in an experimental collaborative assembly task setup for four levels of workload composed of a secondary task and task complexity. A user study conducted involving 80 participants was assessed through two constructs especially designed for the evaluation (quality of task execution and usability) and user preferences regarding the LOA modes. Results revealed that the quality of task execution and usability was better at high LOA for low workload. Most of participants also preferred high LOA when the workload increases. However, when complexity existed within the workload, most of the participants preferred the low LOA. The results reveal the benefits of high and low LOA in different workload situations. This study provides insights related to shared control designs and reveals the importance of considering different levels of workload as influenced by secondary tasks and task complexity when designing LOA in human–robot collaborations.

2021 ◽  
Vol 6 (4) ◽  
pp. 7317-7324
Hrishik Mishra ◽  
Ribin Balachandran ◽  
Marco De Stefano ◽  
Christian Ott

2021 ◽  
Vol 11 (15) ◽  
pp. 6950
Mauricio Marcano ◽  
Fabio Tango ◽  
Joseba Sarabia ◽  
Andrea Castellano ◽  
Joshué Pérez ◽  

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

2021 ◽  
Vol 160 ◽  
pp. 106301
Ming Yue ◽  
Chao Fang ◽  
Hongzhi Zhang ◽  
Jinyong Shangguan

2021 ◽  
Vol 6 (4) ◽  
pp. 6305-6312
Wei Dai ◽  
Yaru Liu ◽  
Huimin Lu ◽  
Zhiqiang Zheng ◽  
Zongtan Zhou

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4647
Anh-Tu Nguyen ◽  
Jagat Jyoti Rath ◽  
Chen Lv ◽  
Thierry-Marie Guerra ◽  
Jimmy Lauber

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

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