Design of Autonomous/Man-Machine-Cooperative Mobile Robot

In this paper, we propose a control system design method for a human-machine cooperative system in which a human and machine cooperate to propel a cart to a reference trajectory. In the previously proposed method, the human plays the role of recognizing the environment and determining the propulsive force, while the machine determines the steering angle based on the position and posture of the cart, using a vector field with the reference trajectory as the orbit attractor.However, since this method cannot avoid obstacles on the trajectory, we extend it to a region attractor. By setting an index variable that changes depending on human input, the vector field is designed in the extended dimensional space including the index variable, and the cart can avoid obstacles by moving within the region defined by the reference trajectory with its parallel trajectory depending on human input. In this paper, we describe (1) the design method of the reference trajectory and the parallel trajectory considering the workspace of the steering and the distance between the cart and the wall, (2) the design method of the region attractor in the extended dimension including the index variable, and (3) the experimental verification of the proposed method.

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Control of Human-machine cooperative cart by region attractor to achieve both obstacle avoidance and trajectory tracking

Okada

YOU

Masuya

2022

Transactions of the JSME (In Japanese)

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Automatic ROI Detection and Evaluation in Video Sequences Based on Human Interest

Rokunuzzaman¹,

Sekiyama²,

Fukuda³

2010

J. Robot. Mechatron.

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This paper describes an approach for automatic detection and evaluation of Region of Interest (ROI) based on human psychology of interest and relevance. It is very challenging to determine the cognitive boundary of the scene in real time due to its complexity in decision making. We have proposed a novel method to solve this problem by using human cognitive processes of attention, interest generation and relevancy. Our method successfully determines this cognitive boundary as ROI which is significant by its information content based on interest and relevancy. The effectiveness of ROI detection is checked with Eye tracker system. The highest detection accuracy found is 81.25% which is higher than the existing method. The ROI evaluation is compared with evaluation by human observers. The subjective correlation is found 85% with human evaluation. The experimental results show that our method is imitating human decision making for ROI detection and evaluation.

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Design of Autonomous/Man-Machine-Cooperative Mobile Robot

Cited by 2 publications

References 9 publications

Control of Human-machine cooperative cart by region attractor to achieve both obstacle avoidance and trajectory tracking

Control of Human-machine cooperative cart by region attractor to achieve both obstacle avoidance and trajectory tracking

Automatic ROI Detection and Evaluation in Video Sequences Based on Human Interest

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