Autonomous Mobile Robots in Unknown Outdoor Environments

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.

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.

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.

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.

In this paper, we propose an autonomous navigation for Unmanned Ground Vehicles (UGVs) by a path selection method based on local features of terrain in unknown outdoor rough terrain environment. The correlation between a local terrain feature obtained from a path and a value of the path obtained from the global path planning is extracted in advance. When UGV comes to a branch while it is running, the value of path is estimated using the correlation with local terrain feature. Thereby, UGV navigation is performed by path selection under the criterion of shortest time in unknown outdoor environment. We experimented on a simulator and confirmed that the proposed method can select more effective paths in comparison with a simple path selection method.

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.

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 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.

Autonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs with a need for scalable decentralised solutions, remains a significant challenge. This research introduces a dynamic path planning system for AMRs designed for reactive adaptation to FMS disturbances and generalisation across factory layouts, incorporating support for multiple AMRs with integrated conflict avoidance. The system is built on a Multi-Agent Systems (MAS) architecture, where software AMR agents independently calculate their paths using a hybrid Genetic Algorithm (GA) that employs Cell-Based Decomposition (CBD) and optimises path length, smoothness, and overlap via a multi-objective fitness function. Multi-AMR conflict avoidance is implemented using the Iterative Exclusion Principle (IEP), which facilitates priority-based planning, knowledge sharing through Predictive Collision Avoidance (PCA), and iterative replanning among agents communicating via a blackboard agent. Verification demonstrated the system’s ability to successfully avoid deadlocks for up to nine AMRs and exhibit good scalability. Validation in a simulated FMS environment confirmed robust adaptation to various disturbances, including static and dynamic obstacles, while maintaining stable run times and consistent path quality. These results affirm the practical feasibility of this hybrid GA and MAS-based approach for dynamic AMR control in complex industrial settings.

The second cause of disability in Mexico is visual impairment; 26% population is blind. Even though blind people maintain a lower quality’s life than sighted people, there is not enough social interest to develop comprehensive solutions that improve it. Although many voice-activated emerging technologies use artificial intelligence to get human-machine communication through intelligent virtual assistants, such as Alexa, Siri, Google Home, and Cortana, among others, in reality, there have not been developed specific tools just for blind people that help them to improve their independent behavior because they always depend on other people in doing their daily tasks, more even when they need to move around unknown environments for them. This document reports the development of an autonomous mobility system to be operated by blind people. By audio reconstruction, it is possible to dictate, to the blind pedestrian, in real-time, the presence of traffic lights, crosswalks, and information about their current location. This system employs real-time computer vision tools, artificial intelligence, audio playback systems, and location systems, and it improves the independent behavior of blind people because they could move through unknown environments without the assistance of any sighted person, giving them greater independence and consequently increasing their life’s quality.

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.

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.

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.

With the continuous expansion of international terrorist organizations, terrorists have been looking for ways to cause massive destruction, in which one of dangerous weapons is a radiological source. The explosion of radiological sources makes radioactive particles scattered over a vast area, causing radiation contamination. To avoid radiation disasters, there should be autonomous mobile robots that are equipped with radiation detectors to search any radiological sources in public places. Thus, the motivation of this paper is to find out any radiological sources in an unknown environment by an autonomous mobile robot. To estimate the accurate location of a radiological source, an artificial potential field is adopted where attractive forces are modeled by a particle filter using the model of radiation sensing model. To build up an accurate sensing model, we consider the physics of radiation and the angle of incidence of gamma ray to the radiological sensor. The simulation results show that the mobile robot is able to find the radiological source in an unknown environment.