Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions

&lt;span&gt;A multi-agent robot system (MARS) is one of the most important topics nowadays. The basic task of this system is based on distributive and cooperative work among agents (robots). It combines two important systems; multi-agent system (MAS) and multi-robots system (MRS). MARS has been used in many applications such as navigation, path planning detection systems, negotiation protocol, and cooperative control. Despite the wide applicability, many challenges still need to be solved in this system such as the communication links among agents, obstacle detection, power consumption, and collision avoidance. In this paper, a survey of the motivations, contributions, and limitations for the researchers in the MARS field is presented and illustrated. Therefore, this paper aims at introducing new study directions in the field of MARS.&lt;/span&gt; 

A review of multi-agent mobile robot systems applications

Blind people present dificulties for reaching objects of interest in the daily life. In this sense, the integration of a path planning module for assisting blind people in purposeful navigation is noteworthy. In this work, we present an algorithm that leverages the high capability of an embedded computer with graphics processing unit (GPU) NVIDIA Jetson TX2 for computing optimal paths to objects of interest. The algorithm computes the optimal path to the objective, considering changes in the environment and changes in the position of the user. The algorithm is efficient for computing new paths when the environment changes by reusing parts of previous computations. In order to compare the performance, the algorithms were implemented and evaluated in MATLAB, C++ and CUDA, for different size of the grid and percentage of unknown obstacles. We found that the implementation on GPU has a speed up of 20 times W.R.T the implementation in C++ and more than 400 times W.R.T the implementation in MATLAB. These results boost us to integrate this module to our main system based on a stereo camera and a haptic belt and so provide to the user assistance in purposeful navigation at real time.

Path planning for assisting blind people in purposeful navigation

This paper is aimed at a description of effects which have assumptions of specific environment structure on quality of recurrently conducted photogrammetry reconstruction. The theoretical part covers the description of three different assumptions of environment structure and mathematical derivation of two suitable recurrent estimators: one based on Extended Kalman filter and the second one based on Maximum likelihood method. The experimental part is introducing simple virtual environment which is observed by linear camera model and then reconstructed using predefined algorithms and assumptions.

/pdf/effects-of-environment-model-parametrization-on-4szxdn1rb6.pdf

Effects of Environment Model Parametrization on Photogrametry Reconstruction

Abstract To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point pg2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.

Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specifically on processing of data from a heterogeneous set of sensors concurrently. Sensors are considered to be different in a sense of measured physical quantity and so the problem of effective data-fusion is discussed. A special extension of the standard probabilistic approach to SLAM algorithms is presented. This extension is composed of two parts. Firstly is presented general perspective multiple-sensors based SLAM and then thee archetypical special cases are discuses. One archetype provisionally designated as "partially collective mapping" has been analyzed also in a practical perspective because it implies a promising options for implicit map-level data-fusion.

https://ijra.iaescore.com/index.php/IJRA/article/download/20258/12906

General concepts of multi-sensor data-fusion based SLAM

: Machine learning and computer vision are dynamically growing fields, which have proven to be able to solve very complex tasks. They could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. In this paper, we present an overview of the state-of-the-art computer vision and machine learning applications used for bee monitoring. We also demonstrate the potential of those methods as an example of an automated bee counter algorithm. The paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce to them its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use the machine learning techniques for other applications in bee monitoring.

Machine Learning and Computer Vision Techniques in Bee Monitoring Applications

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General concepts of multi-sensor data-fusion based SLAM

Machine Learning and Computer Vision Techniques in Bee Monitoring Applications