Investigation of Control-Authority Allocation for Human-Automation Shared Control in Four-Arm Four-Flipper Disaster Response Robot OCTOPUS

2017 ◽  
Vol 2017 (0) ◽  
pp. 2A1-E11
Author(s):  
Takahiro KATANO ◽  
Mitsuhiro KAMEZAKI ◽  
Taisei KANEKO ◽  
Kohga Azuma ◽  
Kui CHEN ◽  
...  
Keyword(s):  
Disaster Response ◽  
Shared Control ◽  
Control Authority

scholarly journals A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

2020 ◽  
Vol 10 (7) ◽  
pp. 2626 ◽  
Author(s):  
Hanbing Wei ◽  
Yanhong Wu ◽  
Xing Chen ◽  
Jin Xu
Keyword(s):  
Dynamic Model ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Least Square ◽  
Experimental Results ◽  
Shared Control ◽  
Dynamic State ◽  
Dynamic Mode ◽  
Identification Algorithm ◽  
Control Authority

For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

Towards Explainable Co-Robots: Developing Confidence-Based Shared Control Paradigms

2020 ◽  
Author(s):  
Daniel Saraphis ◽  
Vahid Izadi ◽  
Amirhossein Ghasemi
Keyword(s):  
Numerical Simulations ◽  
Shared Control ◽  
Automated System ◽  
Generic Model ◽  
Automation System ◽  
Steering Control ◽  
Control Paradigm ◽  
Control Framework ◽  
Automation Systems ◽  
Control Authority

Abstract In this paper, we aim to develop a shared control framework wherein the control authority is dynamically allocated between the human operator and the automation system. To this end, we have defined a shared control paradigm wherein the blending mechanism uses the confidence between a human and co-robot to allocate the control authority. To capture the confidence between the human and robot, qualitatively, a simple-but-generic model is presented wherein the confidence of human-to-robot and robot-to-human is a function of the human’s performance and robot’s performance. The computed confidence will then be used to adjust the level of autonomy between the two agents dynamically. To validate our novel framework, we propose case studies in which the steering control of a semi-automated system is shared between the human and onboard automation systems. The numerical simulations demonstrate the effectiveness of the proposed shared control paradigms.

Game Theoretic Modeling of a Steering Operation in a Haptic Shared Control Framework

2018 ◽  
Author(s):  
Amir H. Ghasemi
Keyword(s):  
Path Following ◽  
Open Loop ◽  
Shared Control ◽  
Automation System ◽  
Steering Control ◽  
Human Driver ◽  
Control Framework ◽  
Game Theoretic ◽  
Control Authority ◽  
Vehicle Trajectory

Haptic shared control is expected to achieve a smooth collaboration between humans and automated systems, because haptics facilitate mutual communication. This paper addresses a the interaction between the human driver and automation system in a haptic shared control framework using a non-cooperative model predictive game approach. In particular, we focused on a scenario in which both human and automation system detect an obstacle but select different paths for avoiding it. For such a scenario, the open-loop Nash steering control solution is derived and the influence of the human driver’s impedance and path following weights on the vehicle trajectory are investigated. It is shown that by modulating the impedance and the path following weight the control authority can be shifted between the human driver and the automation system.

Shared Control Between Human Driver and Automation in Cooperative Driving Based on Game Theoretic Model Predictive Control

2019 ◽  
Author(s):  
Sangjin Ko ◽  
Reza Langari
Keyword(s):  
Model Predictive Control ◽  
Cooperative Game ◽  
Predictive Control ◽  
Control Strategy ◽  
Shared Control ◽  
Safety Critical ◽  
Cooperative Driving ◽  
Critical Situation ◽  
Human Driver ◽  
Control Authority

Abstract Shared control is a control framework in which control action is shared between a human driver and an automation. In this work, the shared control is studied based on game theoretical approach of distributed model predictive control (DMPC). The solution of cooperative game is derived under DMPC framework, and then realistic driving situation is studied in cooperative game framework. Shared control strategy for fully mixed driving authority is proposed considering collision probability based on TTC (time to collision) and tracking error. Simulations were conducted using Matlab/Simulik in cooperative driving. The simulation scenarios consist of safety critical situation and non-safety critical situation. In safety critical situation, an automation takes more control authority to avoid a collision, and in non-safety critical situation a human driver takes more control authority. The simulation results show that the control authority is shared continuously by the proposed shared control strategy.

scholarly journals Haptic shared control: smoothly shifting control authority?

2011 ◽  
Vol 14 (1) ◽  
pp. 19-28 ◽  
Author(s):  
David A. Abbink ◽  
Mark Mulder ◽  
Erwin R. Boer
Keyword(s):  
Shared Control ◽  
Control Authority

A simplified dynamic model with driver’s NMS characteristic for human-vehicle shared control of autonomous vehicle

2021 ◽  
pp. 095440702110189
Author(s):  
Huateng Wu ◽  
Hanbing Wei ◽  
Zheng Liu ◽  
Jin Xu
Keyword(s):  
Dynamic Model ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Lyapunov Stability Theory ◽  
Shared Control ◽  
Steering Wheel ◽  
Adaptive Genetic Algorithm ◽  
Model Parameters ◽  
Control Authority

Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver’s neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle’s state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver’s NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles.

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