Wall following and human detection for mobile robot surveillance in indoor environment

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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Benchmark Dataset Based on Category Maps with Indoor–Outdoor Mixed Features for Positional Scene Recognition by a Mobile Robot

Robotics ◽

10.3390/robotics9020040 ◽

2020 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Hirokazu Madokoro ◽

Hanwool Woo ◽

Stephanie Nix ◽

Kazuhito Sato

Keyword(s):

Mobile Robot ◽

Visual Information ◽

Indoor Environment ◽

Scene Recognition ◽

Visual Features ◽

Wide Field ◽

Mixed Features ◽

Wide Field Of View ◽

Benchmark Datasets ◽

Unified Method

This study was conducted to develop original benchmark datasets that simultaneously include indoor–outdoor visual features. Indoor visual information related to images includes outdoor features to a degree that varies extremely by time, weather, and season. We obtained time-series scene images using a wide field of view (FOV) camera mounted on a mobile robot moving along a 392-m route in an indoor environment surrounded by transparent glass walls and windows for two directions in three seasons. For this study, we propose a unified method for extracting, characterizing, and recognizing visual landmarks that are robust to human occlusion in a real environment in which robots coexist with people. Using our method, we conducted an evaluation experiment to recognize scenes divided up to 64 zones with fixed intervals. The experimentally obtained results using the datasets revealed the performance and characteristics of meta-parameter optimization, mapping characteristics to category maps, and recognition accuracy. Moreover, we visualized similarities between scene images using category maps. We also identified cluster boundaries obtained from mapping weights.

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Learning from Fuzzy System Demonstration: Autonomous Navigation of Mobile Robot in Static Indoor Environment using Multimodal Deep Learning

2020 24th International Conference on System Theory, Control and Computing (ICSTCC) ◽

10.1109/icstcc50638.2020.9259786 ◽

2020 ◽

Author(s):

Omar Gamal ◽

Xianglin Cai ◽

Hubert Roth

Keyword(s):

Deep Learning ◽

Mobile Robot ◽

Fuzzy System ◽

Autonomous Navigation ◽

Indoor Environment

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