Design, Construction and Control of an Autonomous Mobile Rescue Robot with Visual Feedback

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

This article informally examines the role of stored internal state (that is, memory) in the control of autonomous mobile robots. The difficulties associated with using stored internal state are reviewed. It is argued that the underlying cause of these problems is the implicit predictions contained within the state, and, therefore, many of the problems can be solved by taking care that the internal state contains information only about predictable aspects of the environment. One way of accomplishing this is to maintain internal state only at a high level of abstraction. The resulting information can be used to guide the actions of a robot but should not be used to control these actions directly; local sensor information is still necessary for immediate control. A mechanism to detect and recover from failures is also required. A control architecture embodying these design principles is briefly described. This architecture was successfully used to control real-world and simulated real-world autonomous mobile robots performing complex navigation tasks. The architecture is able to incorporate standard AI planning and world-modeling algorithms into a real-time situated framework.

In this paper, we describe a basic sensing system for self-diagnosing an autonomous mobile robot. In recent years, many researches on intelligent robots and systems have been done. But, when such robots and systems work in the real environment, it is important for those robots and systems to have the ability to recognize their own conditions for detecting faults. On the point of view, we should consider pay more attention to diagnose in such intelligent systems. Therefore we try to construct an internal sensing system as a self-diagnosis system on a real robot. Especially, in this paper, we discuss about motor system of an autonomous omnidirectional mobile robot, which was developed in RIKEN. The self-diagnosis system consists of multiple sensors, which are voltage, current, encoder, and magnetic sensors. We show some diagnosing experimental results using the real system. From the results, we could collect basic data for fault detection of the system.

Research on mobile robots began in the late sixties with the Stanford Research Institute’s pioneering work. Two versions of SHAKEY, an autonomous mobile robot, were built in 1968 and 1971. The main purpose of this project was “to study processes for the realtime control of a robot system that interacts with a complex environment” 〈NIL 69〉. Indeed, mobile robots were and still are a very convenient and powerful support for research on artificial intelligence oriented robotics. They possess the capacity to provide a variety of problems at different levels of generality and difficulty in a large domain including perception, decision making, communication, etc., which all have to be considered within the scope of the specific constraints of robotics: on-line computing, cost considerations, operating ability, and reliability.

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.

We implement an architecture for the planning and control of an intelligent autonomous mobile robot which consists of concurrently running modules forming a hierarchy of control in which lower-level modules perform 'reflexive' tasks while higher-level modules perform tasks requiring greater processing of sensor data. A knowledge-based system performs the task planning and arbitration of lower-level behaviors. This system reasons about behavior selection (fusion) based on its current knowledge and the situation at hand provided by monitoring the status from lower-level behaviors and the map builder. We implement this knowledge-based planning module in CLIPS (C Language Implementation Production System), a rule-based expert systems shell. CLIPS is written in and fully integrated with the C language providing high probability and ease of integration with external systems. We discuss implementation issues including the implementation of control strategy in CLIPS rules and interfacing to other modules through the use of CLIPS user-defined external functions.

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation.

Fuzzy drive control of an autonomous mobile robot

Accurate navigation in outdoor environments requires integrating multiple sensor sources for reliable localization and trajectory tracking. This study proposes Pure Pursuit with Dynamic Steering Control (PP-DSC), which adaptively adjusts both lookahead distance and velocity based on steering angle. The algorithm was deployed on a four-wheeled steering-type autonomous mobile robot (AMR) using Robot Operating System 2 (ROS 2) Jazzy, with real-time sensor fusion from GNSS-RTK, IMU, and wheel encoders. Experiments were conducted on straight, circular, and figure-eight trajectories at 1.0–5.0 m/s in an open area (64 × 20 m). PP-DSC achieved mean lateral deviations of 0.05, 0.07, and 0.08 m respectively, representing 68–82% improvement over standard PP (means 0.19, 0.40, and 0.27 m). To evaluate cross-domain applicability, the algorithm was extended with a Fire and Explosion Index (F&EI)-based safety factor (Safety-integrated PP-DSC) and tested via simulation in an empty fruit bunch (EFB) biodiesel plant (92 × 65 m). Standard PP outperformed Safety-integrated PP-DSC by 15.6% in this industrial setting due to tight turning radii (5–9 m), though Safety-integrated PP-DSC retained advantages in moderate-curvature sections with 11–17% improvement. The F&EI-based safety integration added less than 1% tracking overhead while providing automatic velocity reduction in hazard zones for Process Safety Management (PSM) compliance. The findings confirm that PP-DSC significantly improves trajectory tracking in open-field environments, while industrial deployment requires geometry-specific algorithm selection.

This paper describes position control of autonomous mobile robot using combination of Kalman filter and Fuzzy logic techniques. Both techniques have been used to fuse information from internal and external sensors to navigate a typical mobile robot in an unknown environment. An obstacle avoidance algorithm utilizing stereo vision technique has been implemented for obstacle detection. The odometry errors due to systematic-errors (such as unequal wheel diameter, the effect of the encoder resolution etc.) and/or non-systematic errors (ground plane, wheel-slip etc.) contribute to various motion control problems of the robot. During the robot moves, whether straight-line and/or arc, create the position and orientation errors which depend on systematic and/or non-systematic odometry errors. The main concern in most of the navigating systems is to achieve the real-time and robustness performances to precisely control the robot movements. The objective of this research is to improve the position and the orientation of robot motion. From the simulation and experiments, we prove that the proposed mobile robot moves from start position to goal position with greater accuracy avoiding obstacles.

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.

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.

Recent advances in decision-making algorithms used in mobile robotics require more advanced and adaptive control strategies. Model predictive control (MPC) is one of the prominent strategies to manage diverse kinds of complex dynamic systems. Despite their widespread adoption in industrial robotics owing to their structural simplicity and ease of implementation, proportional-integral-derivative (PID) controllers exhibit notable limitations in effectively addressing process variations and system constraints, particularly those arising from mechanical constraints on joint positions and velocities. As autonomous mobile robots (AMRs) have been increasingly deployed in various and demanding applications, the need for more advanced control algorithms has become critical. In this study, a novel hybrid control framework integrating MPC and PID strategies is proposed and experimentally validated on a real-world differential drive robot, aiming to enhance tracking accuracy and overall operational performance. The system model of the TurtleBot3 robot is identified using the System Identification Toolbox and validated through extensive motion tests on the real robot by using Robot Operating System 2. The proposed control scheme combines the predictive capabilities of MPC with the reactive nature of PID to facilitate improved management of system constraints, aiming to improve the performance of AMR in controlling both linear and angular velocities. Experimental results show that the hybrid MPC-PID controller exhibits better performance by reducing tracking errors while maintaining reliability and robustness characteristics over a conventional PID controller. These results demonstrate that the hybrid MPC-PID approach provides a more effective solution for dynamic control tasks in mobile robotic systems, particularly in scenarios requiring high accuracy and reliability.

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.