ROS Based Autonomous Disinfectant Mobile Robot for Hospitals

Robotics is a scientific discipline which includes the operation and development of robots and other fields of computer science, as well as electrical and mechanical engineering and high-performance modular control systems. This paper reports the first results of a research project that uses a previously developed MES - Modular Electronic System for autonomous mobile robots and for developing robots to use the MES in other scientific applications. This approach offers the ability to build a scalable and flexible control system with only a few modules. A novel feature of our approach is its modular integration of electronics with the ability to multitask, which can be adapted for particular applications. We demonstrate the robustness of this approach in controlling an indoor mobile robot for the EUROBOT 2009 and 2010.

The goal of this research was to develop a fuzzy obstacle avoidance system for an autonomous mobile robot using IR detection sensors. This paper presents implemented control architecture for behavior-based mobile robot. The mobile robot is able to interact with an unknown environment using a reactive strategy determined by sensory information. Current research in robotics aims to build autonomous and intelligent robots, which can plan its motion in a dynamic environment. Autonomous mobile robots are increasingly used in well structured environment such as warehouses, offices and industries. Fuzzy behavior able to make inferences is well suited for mobile robot navigation because of the uncertainty of the environment. A rule-based fuzzy controller with reactive behavior was implemented and tested on a two wheels mobile robot equipped with infrared sensors to perform collision-free navigation. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for small wheeled mobile robots.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

Developing autonomous mobile robots that can coexist with human in populated environments is still considered a big challenge. To address this problem the authors propose a novel scheme to assist mobile robots by providing localization information externally. This scheme combines the autonomous navigation and target tracking research fields to arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment is sensed using a laser range finder and camera based sensor unit. Using Rao-Blackwellized particle filter technique, the robots that need assistance are continuously tracked. In contrast with conventional laser range finder based tracking systems, the placement of sensor is changed to a level above average human height and the mobile robots are modified by attaching a cylindrical pole. The experiments show the validity of the proposed scheme for simultaneous localization assistance for multiple mobile robots. Two mobile robots were simultaneously navigated in given trajectories using assistance data, successfully.

This article proposes a self‐organizing model for pattern learning together with an application to an autonomous mobile robot system. The self‐organizing model consists of a processing rule prescribed and a memory part being blank at the initial stage. To an input signal, the model searches for a similar signal in the memory, and recalls its related information. If the information accompanied with the input signal differs from the recalled information, the model adds the new information to the memory. It influences the subsequent operations. Thus, the model constructs successively a data‐base in a self‐organizing way. This model can universally learn and reproduce any pattern of input‐output response desired. Two principal functions in autonomous movement, i.e., position identification and obstacle avoiding movement were realized based on the self‐organizing model. Furthermore, a camera type autonomous mobile robot system for indoor was made up. The size of the robot is about 0.7 × 0.7 × 0.7 m, and the weight is about 30 kg. The speed of movement is less than 3 km/h. A small computer that has a 16 bit microprocessor and a 1Mbyte RAM controls the motion of the robot with an extendedClanguage.

The purpose of this paper is to detail the development of a mobile robot and control support system. The robot has five infrared (IR) ray sensors and three ultrasonic sensors to enable it to follow a guide person with an IR ray transmitter. The robot follows the IR rays emitted by the transmitter and moves towards its destination. The control system has been developed by using network technology (client/server model) based on the TCP/IP protocol; the CPU of the robot system has a low-enough speed for this. The advanced control strategy for the mobile robot is carried out with a high-speed host server computer connected through the TCP/IP network. In this paper, the autonomous mobile robot system and the control support system aided by the TCP/IP network are described.

It is important for an autonomous mobile robot to explore its surroundings in performing the task of localization and navigation. Because of the complexity of the environment, one simple kind of sensors isn't sufficient for mobile robot to accomplish these tasks. The DSP based non-vision sensor data acquisition system selects DSP TMS320LF2407A as its CPU, and integrates with ultrasonic sensors, infrared sensors, position sensitive detectors and a compass. The diversity of sensors can avoid the blind zone and the specular reflection caused by ultrasonic sensors by multi-sensor data fusion. It communicates with PC through serial ports and USB ports. The system has a simple and flexible architecture, low power consumption. The experimental results on a real mobile robot show its reliability and real-time capability.

The design and implementation of the task control architecture (TCA), a general-purpose framework plus facilities for building and controlling mobile robots, are presented. TCA is designed to support capabilities needed by many autonomous mobile robot systems, including concurrent planning, execution, and perception; reacting to changes in the environment; error recovery; and handling multiple tasks. The facilities that TCA provides for implementing these capabilities, including distributed processing, explicit representation and manipulation of hierarchical plans, concurrent monitoring of selected conditions, and context-dependent exception handling, are described, along with a mobile manipulator that uses the architecture to operate with multiple goals in a dynamic and uncertain environment. TCA has been implemented and is used to run several mobile robot testbeds. >

This article proposes an autonomous mobile robot (AMR) system based on the artificial intelligence of things (AIoT) for collecting garbage. The proposed system consists of an AMR subsystem, a robot operating system (ROS) robot subsystem, an AI garbage recognition subsystem, solar power supply subsystem, and a smart trash can subsystem. The AMR subsystem uses a motor drive, which is built on an aluminum alloy metal base and is connected to an AI edge computing module via an app, to control the robot. The ROS subsystem is built on an edge computing module, which is coupled with the motor drive module to control the robot arm and the robot gripper, and is connected to the AI edge computing module to control the robot arm through the app. The AI garbage recognition subsystem recognizes the types of waste in real time through an image sensor (camera). This subsystem adopts multiple sensors that can monitor the level of waste in the trash can. Moreover, a map on a web platform locates all trash cans. Servomotors enable automatic opening and closing of the lid to prevent the unpleasant smell of garbage from wafting. A user connects to the AI edge computing module through an app to monitor the robot and control the movement of the robot to any location on the map.

Open Control Architecture for Mobile Robot

In recent years, most of the research about autonomous mobile robotics is focused on obstacle detection and path planning in unknown environments. Improved accuracy in environment recognition and high precision in robot control can ensure feasibility of intelligent and autonomous mobile robotic systems in unexplored environments. To reach this goal, effective navigation and control approaches for the mobile robots must be proposed. This paper presents a path planning approach based on the navigation map reconstructed by a scout robot equipped with an onboard camera using VSLAM (visual simultaneous localization and mapping) algorithm. In the region where surveillance cameras can see blind robots equipped with no onboard cameras, these blind robots can be driven to follow the desired trajectories and reach their goal positions by the proposed visual servo control law. The proposed navigation and control system has been validated by experimenting with a blind robot using available real-time vision from the surveillance cameras.

Most path planning approaches (global, reactive, or hybrid) existing in literature don’t consider the transfer of experience concept between agents operating in the same environment. A mobile robot executes any planning method to reach its goal, at the time of navigation, it explores the environment, detects the location of obstacles, makes its calculations to choose the best decisions. All this experience gained by the robot will be wasted, and if a new robot has just joined the environment, it has to repeat the entire planning process without benefiting from past experiences. Our contribution consists in: the use of a Holonic Multi-Agent system for modeling the two abstraction levels of the proposed system, an enhanced version of Ant Colony Optimization is used, where the ants are considered as autonomous and heterogeneous robots’ agents. Each ant in the system executes a distinct planning method to reach its goal, on its return path, it puts some pheromones in its experience memory table, which will be transferred to the master agent which incarnates the collective experiences of all agents that have passed through the platform. The simulation and comparison results have confirmed that using the proposed system, the execution time is significantly decreased and the robot safely achieves its goal.

Simultaneous Localization and Mapping SLAM is one of the most important capabilities for autonomous mobile robots, and many researches have been proposed demonstrating the effective SLAM methods. However, these SLAM methods sometimes require assumptions such as the sensor model, which is difficult to implement and use the SLAM methods. In our previous work, a SLAM method based on Evolution Strategy ES was proposed and the on-line SLAM in indoor environments was realized. However, the definition of the map building method was not clear. Therefore, we propose a SLAM method based on a simple map building and search method. In this paper, we explain our autonomous mobile robot system and propose our SLAM method based on µ+1-ES. The experimental results show the effectiveness of the proposed method.

We have previously developed a navigation system for autonomous mobile robots. It can find landmarks on the ceiling to move and work. However it is impossible to recognize landmark in case that the size of landmark is different from the size of template. In such cases, the robot cannot continue to work. Therefor we proposed Variable Template Matching (VTM) which makes it possible to recognize landmarks of different sizes. We realize VTM by using the optimization method called Evolution Strategy (ES). The robot can recognize the landmark faster by binary density projection to infer landmark parameters. Minus Fuzzy Template is useful for the robot to avoid misidentifying.

This paper introduces a cost-effective autonomous charging docking system that utilizes a monocular camera and ArUco markers. Traditional monocular vision-based approaches, such as SolvePnP, are sensitive to viewing angles, lighting conditions, and camera calibration errors, limiting the accuracy of spatial estimation. To address these challenges, we propose a regression-based method that learns geometric features from variations in marker size and shape to estimate distance and orientation accurately. The proposed model is trained using ground-truth data collected from a LiDAR sensor, while real-time operation is performed using only monocular input. Experimental results show that the proposed system achieves a mean distance error of 1.18 cm and a mean orientation error of 3.11°, significantly outperforming SolvePnP, which exhibits errors of 58.54 cm and 6.64°, respectively. In real-world docking tests, the system achieves a final average docking position error of 2 cm and an orientation error of 3.07°, demonstrating that reliable and accurate performance can be attained using low-cost, vision-only hardware. This system offers a practical and scalable solution for industrial applications.