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.

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.

Adaptive Haptic Shared Control Framework Using Markov Decision Processing

2018 ◽  
Author(s):  
Amir H. Ghasemi ◽  
Hossein Rastgoftar
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Human Perception ◽  
Shared Control ◽  
Manual Control ◽  
Automation System ◽  
Human Driver ◽  
Control Framework ◽  
Control Scheme ◽  
Markov Decision

Semi-autonomous steering promises to combine the best of human perception, planning, and manual control with the precision of automatic control. This paper presents an adaptive haptic shared control scheme using Markov Decision Process (MDP) to keep human drivers in the loop yet free attention and avoid automation pitfalls. Using MDP, algorithms are developed to support the negotiation of authority between the human driver and 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.

Who’s the boss? Arbitrating control authority between a human driver and automation system

2020 ◽  
Vol 68 ◽  
pp. 144-160 ◽  
Author(s):  
Akshay Bhardwaj ◽  
Amir H. Ghasemi ◽  
Yingshi Zheng ◽  
Huckleberry Febbo ◽  
Paramsothy Jayakumar ◽  
...  
Keyword(s):  
Automation System ◽  
Human Driver ◽  
Control Authority

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

2021 ◽  
pp. 095440702097682
Author(s):  
Jinxiang Wang ◽  
Zhenwu Fang ◽  
Mengmeng Dai ◽  
Guodong Yin ◽  
Jingjing Xia ◽  
...  
Keyword(s):  
Path Following ◽  
Vehicle Speed ◽  
Time Varying ◽  
Steering Control ◽  
Characteristic Parameters ◽  
Parameter Uncertainties ◽  
Large Curvature ◽  
Steering Characteristics ◽  
Dynamic Compensator ◽  
Takagi Sugeno

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Feedback control framework for car-like robots using the unicycle controllers

Robotica ◽  
2011 ◽  
Vol 30 (4) ◽  
pp. 517-535 ◽  
Author(s):  
Maciej Michałek ◽  
Krzysztof Kozłowski
Keyword(s):  
Stability Analysis ◽  
Feedback Control ◽  
Closed Loop ◽  
Path Following ◽  
Rear Wheel ◽  
Front Wheel ◽  
Steering Angle ◽  
Formal Stability ◽  
Control Framework ◽  
Error Dynamics

SUMMARYThe paper introduces a novel general feedback control framework, which allows applying the motion controllers originally dedicated for the unicycle model to the motion task realization for the car-like kinematics. The concept is formulated for two practically meaningful motorizations: with a front-wheel driven and with a rear-wheel driven. All the three possible steering angle domains for car-like robots—limited and unlimited ones—are treated. Description of the method is complemented by the formal stability analysis of the closed-loop error dynamics. The effectiveness of the method and its limitations have been illustrated by numerous simulations conducted for the three main control tasks, namely, for trajectory tracking, path following, and set-point regulation.

scholarly journals Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985978
Author(s):  
Ja-Ho Seo ◽  
Kwang-Seok Oh ◽  
Hong-Jun Noh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Steering System ◽  
Driver Model ◽  
Control Technique ◽  
Change Scenario ◽  
Steering Control ◽  
Human Driver ◽  
Steering Effort

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

Sign in / Sign up

Export Citation Format

Share Document