Negotiating the Steering Control Authority within Haptic Shared Control Framework

2020 ◽  
Author(s):  
Vahid Izadi ◽  
Amir H. Ghasemi ◽  
Pouria Karimi Shahri
Keyword(s):  
Shared Control ◽  
Steering Control ◽  
Control Framework ◽  
Control Authority

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.

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.

Shared Control Framework Applied for Vehicle Longitudinal Control in Highway Merging Scenarios

2015 ◽  
Author(s):  
Chunshi Guo ◽  
Chouki Sentouh ◽  
Jean-Christophe Popieul ◽  
Boussaad Soualmi ◽  
Jean-Baptiste Haue
Keyword(s):  
Shared Control ◽  
Longitudinal Control ◽  
Control Framework

A hierarchical adaptive control framework of path tracking and roll stability for intelligent heavy vehicle with MPC

2020 ◽  
Vol 234 (13) ◽  
pp. 2933-2946
Author(s):  
Yantao Tian ◽  
Kai Huang ◽  
Xuanhao Cao ◽  
Yulong Liu ◽  
Xuewu Ji
Keyword(s):  
Adaptive Control ◽  
Cooperative Control ◽  
Path Tracking ◽  
Control Mode ◽  
Steering Wheel ◽  
Change Scenario ◽  
Heavy Vehicle ◽  
Steering Control ◽  
Control Framework ◽  
Conflicting Objectives

Roll stability is a major concern in the path tracking process of intelligent heavy vehicles in emergency steering maneuvers. Due to the coupling of lateral and roll motions of heavy vehicle, steering actions for path tracking may come into conflict with that for roll stability. In this paper, a hierarchical adaptive control framework composed of a supervisor, an upper controller and a lower controller is developed to mediate conflicting objectives of path tracking and roll stability via steering control. In the supervisor, path tracking control mode or cooperative control mode of path tracking and roll stability is determined by the predicted rollover index, and a weight function is introduced to balance the control objectives of path tracking and roll stability in cooperative control mode. Then, in order to achieve multi-objective real-time optimization, model predictive control with varying optimization weights is used in the upper controller to calculate the desired front steer angle. The lower controller which integrates the real electrically assisted hydraulic steering system based on Proportional-Integral-Derivative control is designed to control steering wheel angle. Simulation and hardware-in-loop implementation results in double lane change scenario show that the proposed hierarchical adaptive control framework can enhance roll stability in emergency steering maneuvers while keeping the accuracy of path tracking for intelligent heavy vehicle within an acceptable range.

A Shared Control Framework for Human-Multirobot Foraging With Brain-Computer Interface

2021 ◽  
Vol 6 (4) ◽  
pp. 6305-6312
Author(s):  
Wei Dai ◽  
Yaru Liu ◽  
Huimin Lu ◽  
Zhiqiang Zheng ◽  
Zongtan Zhou
Keyword(s):  
Brain Computer Interface ◽  
Shared Control ◽  
Computer Interface ◽  
Control Framework
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