scholarly journals Modified reactive control framework for cooperative mobile robots

1997 ◽  
Author(s):  
Jesus T. Salido ◽  
John M. Dolan ◽  
John B. Hampshire II ◽  
Pradeep K. Khosla
Keyword(s):  
Mobile Robots ◽  
Reactive Control ◽  
Control Framework

Coupled behaviors in the reactive control of cooperating mobile robots

1995 ◽  
Vol 10 (2) ◽  
pp. 161-177 ◽  
Author(s):  
Robert Ghanea-Hercock ◽  
David P. Barnes
Keyword(s):  
Mobile Robots ◽  
Reactive Control

Incorporating Motivation in a Hybrid Robot Architecture

2004 ◽  
Vol 8 (3) ◽  
pp. 269-274 ◽  
Author(s):  
Alexander Stoytchev ◽  
◽  
Ronald C. Arkin
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Reactive Control ◽  
Office Environment ◽  
Robot Architecture ◽  
Unpredictable Environment ◽  
High Level ◽  
Three Components

This paper describes a hybrid mobile robot architecture that addresses three main challenges for robots living in human-inhabited environments: how to operate in dynamic and unpredictable environment, how to deal with high-level human commands, and how to engage human users. The architecture combines three components: deliberative planning, reactive control, and motivational drives. It has been proven useful for controlling mobile robots in man-made environments. Results are reported for a fax delivery mission in a normal office environment.

Real-time navigational control of mobile robots using an artificial neural network

2009 ◽  
Vol 223 (7) ◽  
pp. 1713-1725 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Back Propagation ◽  
Neural Net ◽  
Reactive Control ◽  
Neural Controller ◽  
Time Optimization ◽  
Set Up ◽  
And Control ◽  
Good Agreement

This article deals with the reactive control of an autonomous robot, which moves safely in a crowded real-world unknown environment and reaches a specified target by avoiding static as well as dynamic obstacles. The inputs to the proposed neural controller consist of left, right, and front obstacle distance to its locations and the target angle between a robot and a specified target acquired by an array of sensors. A four-layer neural network has been used to design and develop the neural controller to solve the path and time optimization problem of mobile robots, which deals with cognitive tasks such as learning, adaptation, generalization, and optimization. The back-propagation method is used to train the network. This article analyses the kinematical modelling of mobile robots as well as the design of control systems for the autonomous motion of the robot. Training of the neural net and control performances analysis were carried out in a real experimental set-up. The simulation results are compared with the experimental results and they show very good agreement.

scholarly journals Cognitive Control in Young and Older Adults: Does Mood Matter?

2021 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Linda Truong ◽  
Kesaan Kandasamy ◽  
Lixia Yang
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Cognitive Control ◽  
Mood Induction ◽  
Control Mode ◽  
Proactive Control ◽  
Reactive Control ◽  
Age Related ◽  
Control Framework ◽  
Neutral Mood

The dual mechanisms of control framework (DMC) proposes two modes of cognitive control: proactive and reactive control. In anticipation of an interference event, young adults primarily use a more proactive control mode, whereas older adults tend to use a more reactive one during the event, due to age-related deficits in working memory. The current study aimed to examine the effects of mood induction on cognitive control mode in older (ages 65+) compared to young adults (ages 18–30) with a standard letter-cue (Experiment 1) and a modified face-cue AX-CPT (Experiment 2). Mood induction into negative and/or positive mood versus neutral mood was conducted prior to the cognitive control task. Experiment 1 replicated the typical pattern of proactive control use in young adults and reactive control use in older adults. In Experiment 2, older adults showed comparable proactive control to young adults in their response time (RT). Mood induction showed little effect on cognitive control across the two experiments. These results did not reveal consistent effects of mood (negative or positive) on cognitive control mode in young and older adults, but discovered (or demonstrated) that older adults can engage proactive control when dichotomous face cues (female or male) are used in AX-CPT.

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