Motion Detection of Objects in Home Security Systems Using Binary-Image Comparison Method Based on Robot Operating System 2 and Raspberry Pi

Based on the robot operating system (ROS) simulation enviromenta,this paper presents an open experimental teaching plan for UAV.Through the visualization functions of ROS,environmental information is acquired by using Kinect and other devices,and the UVA dynamics model and 3D visualization model are established.Design 3D environmental track planning algorithm,and through the dynamic demonstration technology,the dynamic environment of the UAV trajectory planning dynamic demonstration can be achieved.It enables students to implement unmanned aerial vehicle motion control,track planning and visual programming from open experiments.The ROS-based drone simulation experiment is a typical comprehensive application of technologies such as motion control,planning algorithm and visual programming,which helps students to further study the principle of UAV and the route planning method.The teaching practice has proved that the open teacing program has achieved satisfictory results in teaching practice.

We present software for interfacing FITPix and USB Lite compatible readout electronics using Robot Operating System (ROS). ROS is widely adopted middleware for integration of sensors, processing algorithms and logic to autonomous systems such as robots, unmanned helicopters, and robotic payloads. Thanks to ROS, Timepix detectors can be deployed in automated experiments on platforms spanning from traditional desktop computers to small ARM devices such as Raspberry Pi and Odroid. Acquisition and detector settings can be controlled in Linux shell allowing deployment on headless devices. Using ROS networking capabilities, captured frames and detector control can be transmitted via a network, which allows building distributed processing pipelines. The proposed software is a lightweight package, easily connectable to existing visualization, logging, and processing software in ROS. It offers simple bindings to custom Python and C++ programs for real-time control of the acquisition or processing of the captured frames. Rospix was deployed on NASA suborbital rocket, which was successfully launched on April 4, 2018, from Kwajalein Atoll by Pennsylvania State University. Rospix provides reliable solution for mobile robots where the collected data are used in real time to guide the robot through an environment. We release Rospix using the GitHub platform and welcome the community to contribute on the first project connecting the fields of ionizing radiation imaging and mobile robotics.

In this paper, a bioinspired neural model for detecting object motion based on retina computational machanism is proposed based on synthesizing representative works on modeling retina and incorporating some latest findings on retina mechanisms. To understand more in-depth the retina machanism of motion detection, the spatio-temporal properties of each type of neurons in the retina motion pathway are then mathematically analyzed. And then a set of experiments and quantitative analysis of the experimental results are accomplished for assessing the model performance on motion detection. Experimental results show the effectiveness of this proposed model on detecting object motion. Index Terms - retina neural mechanism, retina neural network, object motion detection, computational model. I. Introduction

The industrial revolution 4.0 is the development of industrial based on the internet, control, robotics, and networking. This study describes the usage of a Robot Operating System (ROS) middleware to answer the challenges of the industrial revolution 4.0 for control the industrial electronic devices and household electronic equipment using Raspberry Pi. ROS has nodes, topics and messages that can be used to manage the Raspberry Pi GPIO pins to be active high (1) or active low (0). The result of this study is the system be able to control the on and off electronic devices through the GPIO pins of Raspberry Pi and relay module based on message commands from the ROS node. The electronic devices had controlled in this study are a fan, light, air conditioner, and room heater that have 110/220 AC voltage.

In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ?camera person.? We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.

While spherical robots are an interesting design that has been studied before, but due to their relative manufacturing complexity there are few such devices commercially available. Given the increased popularity of 3D printers and laser cutters, rapid prototyping has become an integral part of engineering practice. Leveraging these advances, we have developed Spherico. Spherico is an inexpensive spherical robot that utilizes both laser cutter and 3D printing technology combined with off-the-shelve (OTS) components. Driven by a Raspberry Pi, the ROS (Robotic Operating System) is used as the underlying software architecture for controlling the robot. Using all the off-the-shelve OTS components, rapid prototyping parts and ROS, Spherico takes less than 72Hrs to build. The resulting robot is an inexpensive and robust platform for research into spherical robot control.

Object detection on the basis of relative motion was investigated in the fly at the neuronal level. A representative of the figure detection cells (FD-cells), the FD1b-cell, was characterized with respect to its responses to optic flow which simulated the presence of an object during translatory flight. The figure detection cells reside in the fly's third visual neuropil and are believed to play a central role in mediating object-directed turning behaviour. The dynamical response properties as well as the mean response amplitudes of the FD1b-cell depend on the temporal frequency of object motion and on the presence or absence of background motion. The responses of the FD1b-cell to object motion during simulated translatory flight were compared to behavioural responses of the fly as obtained with identical stimuli in a previous study. The behavioural responses could only partly be explained on the basis of the FD1b-cell's responses. Further processing between the third visual neuropil and the final motor output has to be assumed which involves (1) facilitation of the object-induced responses during translatory background motion at moderate temporal frequencies, and (2) inhibition of the object-induced turning responses during translatory background motion at high temporal frequencies.

This paper presents methods for motion detection and estimation of objects in a scene captured by a moving camera. A new iterative algorithm is developed and presented for the correction of geometrical distortion caused by global motion in a scene. A binary hypotheses test is subsequently established to classify the pixels in the corrected image as either locally moving (object motion) or not moving (stationary). The developed method incorporates estimates of additive white Gaussian noise in all steps and is therefore more robust than simple change detection method.

Abstract—This paper presents GAMYA, a cost-effective autonomous navigation vehicle developed using the Robot Operating System 2 (ROS2) framework. The system integrates a Raspberry Pi 4 as the central processing unit, an Arduino Uno R3 for motor control, and a 2D LiDAR sensor for environmental perception. GAMYA is designed to perform real-time mapping, localization, and obstacle avoidance through the implementation of the ROS2 Navigation2 stack and SLAM Toolbox. A differential drive mechanism is employed, and odometry is computed using encoder feedback to support accurate navigation. The system is tested in a simulated environment using Gazebo, where it successfully navigates dynamic and static obstacles. Results demonstrate reliable autonomous performance with minimal latency, showcasing the potential of using low-cost components to develop robust robotic platforms suitable for academic and research applications. Keywords—Autonomous Navigation, ROS2, LiDAR, SLAM, Gazebo Simulation, obstacle avoidance, Raspberry Pi, Arduino, Mobile Robot

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.

In recent years, automation and robotics have played a vital role in improving healthcare efficiency and reducing human workload. The proposed system, “Intelligent Mobile Robotics System for Healthcare and Patient Support,” focuses on developing a low-cost mobile robotic platform capable of performing autonomous navigation and medicine delivery tasks within hospital environments. The project involves designing, simulating, and controlling a medical robot capable of autonomous movement between predefined locations using the Robot Operating System (ROS) and Gazebo simulation environment. The robot is built using an ESP32 microcontroller, TS7960 high-power motor driver, 12V 300 RPM Johnson DC motors, and a 12V Li-ion rechargeable battery pack. The robot’s motion is controlled via laptop communication, while path planning and mapping are simulated virtually in Gazebo. The proposed design provides a foundation for a smart, autonomous delivery system that can safely transport medicines and medical supplies without direct human contact. In future improvements, LiDAR and Raspberry Pi will be integrated for real-time mapping, object detection, and full autonomy. This approach demonstrates an affordable, scalable solution for healthcare automation, particularly useful in hospitals, quarantine zones, and patient care environments.

In order to improve the working efficiency of the automatic search robot, a ROS automatic search robot based on voice interaction and object recognition is designed. The design uses the STM32 control board as the core controller for the lower chassis, and applies the PID algorithm and the track deduction algorithm to achieve the motion control and spatial positioning of the robot; in the upper brain control, the Raspberry Pi 3 installed with the ROS (Robot Operating System) is used as the core controller, which can realize the voice interaction function and satisfy the need to search for target object in a specific environment. It makes the automatic search robot more intelligent and humane.

The legged mobile manipulator consists of a multi-legged robot mounted with a robotic arm. The advantage of this robotic system is that it has more mobility and manipulability compared to wheeled mobile manipulator. In this work, a legged mobile manipulator is developed for education and research purpose at low cost, which is named as SiMMER (Six-legged Mobile Manipulator for Education and Research). Then, a novel control strategy is proposed for the coordinated movement of all the joints to execute a given task in Cartesian space. Robot Operating System, a Raspberry Pi and two PCA9685 16-Channel 12-bit PWM Servo Motor Driver I2C Modules are used to control the robot. Finally, the control strategy is implemented on a physical prototype. The experimental results show that all the joints are moving as desired, so that the end-effector is able to follow the given trajectory.

This paper presents the development of a Robot Operating System (ROS)-based mobile robot control using electromyography (EMG) signals. The proposed robot’s structure is specifically designed to provide modularity and is controlled by a Raspberry Pi 3 running on top of an ROS application and a Teensy microcontroller. The EMG muscle commands are sent to the robot with hand gestures that are captured using a Thalmic Myo Armband and recognized using a k-Nearest Neighbour (k-NN) classifier. The robot’s performance is evaluated by navigating it through specific paths while solely controlling it through the EMG signals and using the collision avoidance approach. Thus, this paper aims to expand the research on the topic, introducing a more accurate classification system with a wider set of gestures, hoping to come closer to a usable real-life application.

With the development of the Virtual Reality technology and the next Human-Machine Interaction technology, this paper focus on the object motion detection and object skin color analysis, provide one kind of hand gesture segmentation method based on one camera. This method capture the image from the single camera to detect the moving object by the time difference method and the Gaussian module method, tracking the hand motion region real time, then to segment the hand gesture using the specified region skin color features after the hand region is extracted. Using the motion detection and the skin color features both, to do static gesture recognition by the template match method after extracting the features of the static gesture contour.This experiment make clear that the segmentation has better effect and recognition result.