Intelligent Mobile Robot Research Articles

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile Robot

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile Robot

Detection of Intelligent Intruders in Wireless Sensor Networks

Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile Robot

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile Robot

Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer

This paper develops an indoor intelligent service mobile robot that has multiple functions, can recognize and grip the target object, avoid obstacles, and accurately localize via relative position. The locating method of the robot uses the output values of the sensor module, which includes data from a gyroscope and a magnetometer, to correct the current rotation direction angle of the robot. An angle correction method can be divided into three parts. The first part calculates the angle values obtained from the gyroscope and the magnetometer that are installed on the robot. The second part explores the error characteristics between the sensor module and the actual rotation direction angle of the robot. The third part uses the error characteristic data to design the fuzzy rule base and the Kalman filter to eliminate errors and to get a more accurate orientation angle. These errors can be described as either regular or irregular. The former can be eliminated by fuzzy algorithm compensation, and the latter can be eliminated by the Kalman filter. The contribution of this paper is to propose an error correction method between the calculus rotation angle determined by the sensor and the actual rotation angle of the robot such that the three moving paths, i.e., specified, actual, and calculus paths, have more accurate approximation. The experimental results demonstrate that the combination of fuzzy compensation and the Kalman filter is an accurate correction method.

DEVELOPMENT OF TRACKING SYSTEM FOR MOBILE ROBOT USING IMAGE RECOGNITION ALGORITHMS

In this paper, an intelligent mobile robot using image processing technology is developed. The mobile robot contains an image system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC-based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The image processing based on OpenCV uses two different tracking methods to track moving targets: MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection). The efficiency of both methods for tracking the moving target on the mobile robot is compared here. The balance control system, with a HOLTEK Semiconductor Company’s HT66F Series 8-bit microprocessor as the processor, uses the PID control law according to the feedback signals of the inclinometer sensor to control the balance level of the loading platform.

Scene object recognition for mobile robots through Semantic Knowledge and Probabilistic Graphical Models

Scene object recognition is an essential requirement for intelligent mobile robots. In addition to geometric or appearance features, modern recognition systems strive to incorporate contextual information, normally modelled through Probabilistic Graphical Models (PGMs) or Semantic Knowledge (SK). However, these approaches, separately, show some weaknesses that limit their application, e.g., the exponential complexity of the probabilistic inference over PGMs or the inability of SK to handle uncertainty. This paper presents a hybrid PGM-SK system for object recognition that integrates both techniques reducing their individual limitations and gaining in probabilistic inference efficiency, performance robustness, uncertainty handling, and providing coherent results according to domain knowledge codified by a human expert. We support this claim with an extensive experimental evaluation according to both recognition success and time requirements in real scenarios from two datasets (NYU2 and UMA-offices). The yielded figures support the suitability of the hybrid PGM-SK recognition system, and its applicability to mobile robotic agents.

A Novel Method of Mobile Robot Fire Detection and Tracing Using Proportional Derivative (PD) Algorithm

One of the problems in intelligent mobile robot fire fighting contest is how the robot can find and can extinguish a fire without alot of sensors. The fire is located in a white circle area at one of the corner in room with random combinations. The way to solve this problems is conventional method-called-wall tracing. The robot uses wall tracing method to navigate, when entering the room this method still in used by the robot until its find the fire spot then uses one additional sensor to detect the white circle area where the fire located at. After detecting the white circle area, the robot will extinguishes the fire. This paper proposed a new method as optimization of conventional method. The proposed method is implementation of current algorithm (proportional derivatives algorithm) not only to wall tracing but also to TPA81 sensor (fire tracing) -called-wall and fire tracing method. Furthermore, this method is combination method between wall tracing and fire tracing. The robot will approach directly to the fire without wall tracing and without detecting white circle area. Then, the robot extinguishes the fire quickly. The robot uses only TPA81 sensor that can be used to detects the fire spot and its position in the room. Test result shows that 50% of the fire position variation on Indonesian Intelligent Robot Contest can be optimized using this method.

Development of a Tracking System on the Mobile Robot

The purpose of this paper is to develop an intelligent mobile robot using image processing technology. The mobile robot is composed of a visual tracking system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The Image processing based on OpenCV use two different tracking methods, MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection), to track moving targets. The efficiencies of both methods for tracking the moving target on the mobile robot are compared in this paper. The loading platform control system uses HOLTEK Semiconductor Company's HT66F Series 8-bit microprocessor as the processor, and receives the feedback data from the FAS-A inclinometer sensor. The controller of the loading platform uses the PID control law according to the feedback signals of the inclinometer sensor, and controls the rotation speed of the platform motor to tune the balance level. Keywords— Intelligent mobile robot, Image processing, OpenCV, MTLT, TLD, HOLTEK, FAS-A inclinometer sensor, PID control.

Robot semantic mapping through human activity recognition: A wearable sensing and computing approach

Semantic information can help robots understand unknown environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a new robot semantic mapping approach through human activity recognition in a human–robot coexisting environment. An intelligent mobile robot platform called ASCCbot creates a metric map while wearable motion sensors attached to the human body are used to recognize human activities. Combining pre-learned models of activity–furniture correlation and location–furniture correlation, the robot determines the probability distribution of the furniture types through a Bayesian framework and labels them on the metric map. Computer simulations and real experiments demonstrate that the proposed approach is able to create a semantic map of an indoor environment effectively.

Hierarchical Hybrid Control with Classical Planning and Trajectory Optimization

One method of control of cyberphysical systems, particularly intelligent mobile robots, involves a hierarchy of high-level classical planning and low-level trajectory optimization. We present a new technique to incorporate trajectory costs in a high-level classical planning problem (CPP). The proposed approach is particularly suited to leverage existing algorithms for solving CPPs and, independently, algorithms for trajectory optimization. To this end, we introduce a family of graphs called lifted planning graphs parametrized by an integer H, and we map paths in these graphs to solutions of the CPP. We show that the overall cost of a high-level plan is a nonincreasing function of H, and that there exists a finite H for which an optimal path in the lifted planning graph is associated with the optimal solution of the CPP. For computational speed and future real-time implementation, we discuss incremental modification of paths in the lifted planning graphs for increasing values of H. We illustrate the proposed ideas with a numerical simulation example involving classical planning for Dubins vehicle routing.

Design of Multi-Intelligent Robot Wireless Communication System Based on ZigBee Automation Control

This paper designs a new ZigBee intelligent robot communication control system, which realizes one to many data transmission. Upper computer can send commands to control multiple mobile intelligent robots to do the action response through wireless module. Slave computer can also send data to host computer to achieve remote monitoring, and has high relay ability. In order to verify the effectiveness and reliability of the system, this paper tests the system and obtains 3D signal and robot node control distribution. Through the analysis of the system time delay and control signal features, we found that the system has good stability, which can satisfy the design need of multi-intelligent robot control. It provides the theoretical reference for the study of ZigBee automation control.

Sensor-Based Intelligent Mobile Robot Navigation in Unknown Environments

Sensor-Based Intelligent Mobile Robot Navigation in Unknown Environments

REAPER: A Reflexive Architecture for Perceptive Agents

This article describes the winning entries in the 2000 Association for the Advancement of Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. In the Hors d'Oeuvres, Anyone? event, a team of robots entertained the crowd while they interactively served cookies; and in the Urban Search-and-Rescue event, a single robot autonomously explored a section of the test area, identified interesting features, built an annotated map, and exited the test area within the allotted time.

Multisensor Data Fusion and Integration for Mobile Robots: A Review

One of the most important and useful feature of autonomous mobile robots is their ability to adopt themselves to operate in unstructured environment. Today robots are performing autonomously in industrial floor, office environments, as well as in crowded public places where the robots need to maintain their localization and mapping parameters.The basic requirement of an intelligent mobile robot is to develop and maintain localization and mapping parameters to complete the complex missions. In such situations, several difficulties arise in due to the inaccuracies and uncertainties in sensor measurements. Various techniques are there to handle such noises where the multisensor data fusion is not the exceptional one.From the last two decades, multisensor data fusions in mobile robots become a dominant paradigm due to its potential advantages like reduction in uncertainty, increase in accuracy and reliability and reduction of cost.This paper presents the reviews of autonomous mobile robots and role of multisenosr data fusion.

Intelligent Robots with MCU Communication System Based on MATLAB

In order to obtain road information to process and requiring movement precision for intelligent mobile robots, we propose solution based on data collection and analysis to control motor. First, we introduce the basic method of serial communication on Matlab. Then, we design PC serial communication and test platform with step motor in Matlab environment. Finally, we can control the mobile robots’ motor driver by processing external data with Matlab. The results show that the solution improves the data-process ability of intelligent sweep-robots, greatly simplifies the development process, improves the efficiency of system development, and can be humanized to write and modify mobile robots’ motion path by PC.

A Algorithm of Path Planning Based on Multiple Mobile Robots

The environment of automated warehouse is complex. Path collision exists conflict between intelligent robot with unknown obstacles and intelligent mobile-robot, increasing the difficulty in multiple mobile robots path planning.To solve the problem, firstly working environmental model is established with traffic rules method and the grid method. Then the whole system adopts the idea of hierarchical cooperation for dynamic local and global path planning.By simulation this method is suitable for dynamic environment with known and unknown obstacles and effectively solve the problem of path planning for multiple mobile robots in automated warehouse.

A Study on Design of Intelligent Mobile Robot based on Localization Sensor for Unmanned Transport Logistics

A Study on Design of Intelligent Mobile Robot based on Localization Sensor for Unmanned Transport Logistics

Ключевые вопросы создания интеллектуальных мобильных роботов

Ключевые вопросы создания интеллектуальных мобильных роботов

The Evaluation of Provided Methods in SLAM Problem and a Method Development in Order to Use in Multi Robot

Simultaneously Localization and Mapping (SLAM) in mobile robots is an issue which is of utmost importance to Robotic Science researchers. The goal is to determine the position of intelligent mobile robots and their navigation to provide the overview map of the environment when no knowledge of it exists. So far, various methods have been presented in group robots to solve the SLAM problem. After about six years of research on mobile robots in Yazd Robotic Association and of applying available algorithms on rescue real robots, we will review major approaches on a robot and will introduce a new method that has been applied on two platforms of manual and autonomous robots in IranOpen 2011 competitions. We provide a method based on ROA-BlackWelized Particles filter (RBPF) algorithm and Forgetting curve in Multi robots which, compared to the limitations of collecting environmental data in the past two years, has provided satisfactory results. In the end, we simulate the consequence method in order to provide the robot’s overview and local maps from the environment in PlayerStage software.

Developing and Research on Experiment Device Based on STC90C51 SCM

In the paper, a SCM experiment device is developed by STC90C51 SCM. The modularizing design idea is adopted for the device. It is composed of five-axis stepping driving module, RS485 communicating module, pulse generator module, USB interface module, double-color lattice and driving module, DA/AD conversion module, dynamic digital dishplay module, temperature testing and infrared receiving module, key-press module, I/O expanding module and so on. The experiment device provides a very good experiment platform for the learner of 51 SCM, and may be a developing board used for the development of numerical control system and intelligent mobile robot. It has a good practical value.