Highly Adaptable Hardware Architecture for Scientific and Industrial Mobile Robots

To lessen the spread of COVID-19 and other dangerous bacteria and viruses, contactless distribution of different items has gained widespread popularity. In order to complete delivery tasks at a catering facility, this paper explores the development of an autonomous mobile robot. The robot, in particular, plans its path and maintains smooth and flexible mobility using a Time Elastic Band (TEB) motion control method and an upgraded Dijkstra algorithm. On the open-source AI platform of iFLYTEK, a voice recognition module was trained to recognize voice signals of different tones and loudness, and an image recognition capability was attained using YOLOv4 and SIFT. The UCAR intelligent vehicle platform, made available by iFLYTEK, served as the foundation for the development of the mobile robot system. The robot took part in China’s 16th National University Student Intelligent Car Race, an experimental demonstration test of the developed mobile robotics. The results of the experiments and task tests demonstrated that the proposed robot architecture was workable. In addition, we designed and put together a mobile robot utilizing components from the Taobao website. Compared to UCAR, this robot is less expensive and has the flexibility to be used in a variety of real-world settings.

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 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.

Robotic systems have significantly transformed the landscape of minimally invasive surgery, offering enhanced precision, reduced recovery times, and improved patient outcomes. These systems have evolved from simple mechanical tools to sophisticated platforms integrating advanced control algorithms and real-time feedback mechanisms. This paper provides a comprehensive overview of the role of robotic systems in minimally invasive surgery, exploring the various benefits, inherent risks, and the challenges associated with their adoption and development. We examine the technical aspects of robotic control systems, such as kinematics, force control, and haptic feedback, which enable greater surgical accuracy. Despite these advantages, challenges such as system complexity, high costs, and ethical concerns remain significant barriers to widespread adoption. The future of robotic surgery lies in overcoming these obstacles, with advancements in system miniaturization and further refinement of control systems poised to enhance the efficacy and accessibility of robotic platforms. This paper explores the role of robotic systems in minimally invasive surgery, focusing on three key areas: bronchoscopy and ureteroscopy. By reviewing the benefits, risks, case studies, and future directions of these technologies, the paper seeks to provide an in-depth understanding of how robotic systems are shaping the future of surgery. Key words: Robotic Surgery, Minimally Invasive Surgery, Robotic Control Systems, Surgical Precision, PID Control, Kinematics, Inverse Kinematics, Forward Kinematics, Dynamic Control, Force Control, Impedance Control, Surgical Robotics, Haptic Feedback, Robotic Arm, Robotic Surgery Benefits, Robotic Surgery Risks, Robot-Assisted Surgery, Surgeon Console, Real-Time Feedback, Surgical Training, Robotic System Development, System Testing, Clinical Trials, Surgical Robot Performance, Robotics in Urology, Telepresence Surgery, Robotic Surgery Cost, Robotic Surgery Future Directions.

The increased usage of mobile robots in the industrial context entails higher safety risks for employees on the production floor. To enable safety, the usage of security concepts on industrial mobile robots is essential. One step towards security is authentication that is necessary to prevent unauthorized people from manipulating an industrial mobile robot’s software or configuration. Traditional authentication schemes that utilize username and password are not feasible for industrial mobile robots as either (a) a remote connection would be necessary to check the credentials or (b) the credentials need to be checked locally on the robot. Remote connections are problematic due to connectivity problems similar to them at RoboCup Logistics League competitions. If the credentials need to be checked on the robot, the usernames and passwords of all authorized people need to be stored and maintained there. As both possibilities are not feasible for industrial mobile robots, we propose an offline authentication approach that uses biometric data to authenticate a user on a mobile robot. The approach uses expiring passwords and a smart card to authenticate authorized people on the mobile robot. The smart card is equipped with a fingerprint reader to check that only authorized people are allowed to authenticate at a mobile robot. To show that the approach is able to provide secured authentication, a threat analysis is performed.

A distributed control architecture for an autonomous mobile robot

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.

Considering the real employment situation of the disabled, it was concluded that there is demand for a robot system that can be used in a real manufacturing environment. To derive our mission statement, we conducted a survey with three criteria: `A. to assist the disabled as much as possible'; `B. to consider the real situation of employment for the disabled'; and `C. to provide assistance for the most necessary tasks for the disabled in the work space'. Based on the survey results, our goal can be stated as follows: To develop work assistive mobile robots to help persons with limb disabilities obtain jobs in manufacturing environments where they can perform simple manual labor. Here, simple labor includes such tasks as circuit inspection and moving boxes. For our goal, we developed two types of work assistive mobile robots, which are small enough to work in the confined environments of real factories. Work assistive mobile robot type I is equipped with a forklift to move boxes and a robotic arm to help with jobs requiring more dexterity. Robot type II is compact in size, adopting an omni-wheel driving mechanism, and is equipped with a lift having a foldable fork. We gathered feedback from empirical work done on the two robots.

To solve the problem of heavy patrol workload and reduce the cost of maintaining public security, it is thought that mobile robots, which substitute for conventional patrolmen, will play an important role in the next decade. This paper describes a mobile patrol robot, including a positioning system using image sensors and a navigation system based on multi-sensor information fusion. The patrol robot can detect some intruders by means of an infrared sensor. If someone intrudes, the robot can shout loudly and light the alarm lamp. Once an emergency appears, the watchman in the safety center can deal with this case according to the environment image obtained from wireless transmission device. The robot, which is provided with an intelligent fire alarm system based on multi-sensor information fusion, can detect the fire precisely and reliably. Finally, assuming the robot will patrol in a flat area, a control algorithm for the patrol robot system is developed, and a field test is conducted around the teaching building to evaluate the constructed robot system. The test results show the robot system can meet the actual requirement for patrol and public security.

This thesis presents the development and integration of a perceptive robotic system to eliminate the risks of manual grinding for foundry operators by providing a flexible automation solution. At foundries across the world, human operators perform manual grinding to remove excess material and transform castings into their final shape. Extensive research has highlighted the significant health risks associated with manual grinding in foundries, such as excessive exposure to airborne dust, noise-induced hearing loss, and severe musculoskeletal damage. To address these concerns, foundries have begun exploring technologies to aid operators ergonomically or automate the grinding process. However, current automation approaches struggle to adapt to casting variation and a wide product mix without real-time input from a human operator. To support the grinding of high variety castings in foundry environments, a robotic system that leverages operator markings to determine where to grind was designed and implemented. Inspired by the previous work of Schimpf et al., the Sketch & Grind Robotic System improves grinding capability by integrating industry-leading hardware with perception and control improvements. Hardware and software developments were necessary for each system component to ensure seamless integration and achieve overall system performance. Current industry practices were investigated to establish an initial abrasive grinding tool for the Sketch & Grind Robotic System. An algorithm within the Sketch & Grind process was developed to enable concave boundaries to mark complex quality issues and avoid obstructions. A complementary process, Scan-N-Plan, was implemented on the Sketch & Grind Robotic System hardware, and the Sketch & Grind process was partially integrated to extend functionality for a foundry implementation. Through these developments, system functionality was realized, and the capabilities of Sketch & Grind were extended. This work supported a broader collaboration between research and industry partners to advance perceptive robotic grinding solutions for foundries. It will culminate in an upcoming industry implementation of the system at a steel foundry.

Abstract. The use of neural networks in automatic control systems makes it possible to increase their compliance with the assigned tasks. Such automatic control systems are able not only to use the general control methods, but also offer their algorithmic approaches to solving the tasks related to the problem of identifying the parameters of the control object. In the article, adaptive control systems and control systems using neural networks are considered. An analysis of recent research in this direction has been conducted. A concept has been proposed for the development of an energy-efficient intelligent automatic control system for a multi-motor electric drive of a mobile industrial robot. Keywords: automatic control system, mobile industrial robot, nonlinearity, energy-efficiency, uncertainty, adaptability, neural network, mechatronic module.

With the advancement of intelligent manufacturing and flexible automation, mobile industrial robots are increasingly being deployed in scenarios such as material handling, inspection, and collaborative operations. As one of the core technologies for mobile robots, the localization system has a direct impact on the stability and accuracy of path planning and task execution. This paper, built on the Robot Operating System (ROS) platform, systematically reviews and analyzes the implementation mechanisms and applicable scenarios of mainstream localization algorithms, with a focus on the performance characteristics of the Extended Kalman Filter (EKF) and Adaptive Monte Carlo Localization (AMCL) in industrial settings. By constructing an experimental platform that fuses multiple sensors—lidar, IMU, and wheel odometry—a series of tests comparing localization accuracy and robustness are conducted. We evaluate each algorithm's adaptability to complex conditions including dynamic occlusions, uniform environmental textures, and multipath interference. The results indicate that AMCL achieves higher positioning accuracy in static, structured environments, whereas EKF is better suited to dynamic applications suffering from sensor drift and data latency. Finally, we propose an optimization approach that integrates visual SLAM and deep-learning–based feature extraction, offering guidance for designing highly reliable localization systems for future industrial robots.

Majority of the advancements, which have been made in the field of robotics are employed in household applications. Surveillance systems are used in guarding and security applications of household and workplaces. This paper presents an advanced surveillance system which integrates sensor system, robot technology and intelligent programming. Robotic surveillance system can be used to replace human guards in dangerous surveillance conditions. Several robots have been developed to assist in these operations. But the full scale employment of robots in crucial fields such as surveillance, security in defence industries and military bases is very limited. The system's overall structure is described, along with descriptions of each component. The robotic system consists of robot eyes, which are capable of sensing an object through IR sensor capability. This robotic system helps in the identification of human presence and alerting respective owners. These operations, performed by the robotic system are achieved using sensors, several number of servo motors, Arduino microcontroller. These components are placed in their respective locations, within a body which is fabricated using the 3D printing technology. The sensors in the robotic system capture the movements of objects. The robotic eyes are fitted with cameras which capture the movements. The robotic system is programmed in such a way that the robotic eyes, move in the direction of object which has been sensed by the sensors and focus on it. If there are multiple moving objects, then one robotic eye focuses on one object, while the other robotic eye focuses on another object. The captured surveillance footage can be viewed through a monitor connected to the robotic system.

Mobile robotics is a challenging research area, with produced results that were unthinkable several years ago. There exist algorithms and methods capable of performing difficult tasks such as detect/classify objects, skill learning and SLAM. From the initial design steps, the real-time software architecture of a robotic platform requires great attention. The problem is difficult, because various components, such as sensing, perception, localization, and motor control, are required to operate and interact in real-time. This makes the system a very complex one. This paper presents a real-time control architecture designed for mobile robots and intelligent vehicles. Moreover, an example of application of the control structure consisting on a system for learning to classify places, using laser range data, is reported.

User-centered analysis for the implementation of industrial mobile robots