Control Of Autonomous Mobile Robot Research Articles

Fuzzy Neural LSTM-RBLS for Fractional-Order PID Sliding-Mode Motion Control of Autonomous Mobile Robots with Four ISID Wheels

Fuzzy Neural LSTM-RBLS for Fractional-Order PID Sliding-Mode Motion Control of Autonomous Mobile Robots with Four ISID Wheels

Active Inference With Empathy Mechanism for Socially Behaved Artificial Agents in Diverse Situations.

This article proposes a method for an artificial agent to behave in a social manner. Although defining proper social behavior is difficult because it differs from situation to situation, the agent following the proposed method adaptively behaves appropriately in each situation by empathizing with the surrounding others. The proposed method is achieved by incorporating empathy into active inference. We evaluated the proposed method regarding control of autonomous mobile robots in diverse situations. From the evaluation results, an agent controlled by the proposed method could behave more adaptively socially than an agent controlled by the standard active inference in the diverse situations. In the case of two agents, the agent controlled with the proposed method behaved in a social way that reduced the other agent's travel distance by 13.7% and increased the margin between the agents by 25.8%, even though it increased the agent's travel distance by 8.2%. Also, the agent controlled with the proposed method behaved more socially when it was surrounded by altruistic others but less socially when it was surrounded by selfish others.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.

A Novel Control and Monitoring Interface Design for ROS Based Mobile Robots

In this study, an interface design was carried out in order to provide convenience to the user in the control and monitoring of the Robot Operating System (ROS) based autonomous mobile robot (AMR). Qt Designer and Python were used in the interface design. Thanks to the designed interface, autonomous and manual control of AMR was provided. Using the gmapping algorithm, the environment in the virtual world was mapped and transformed into a picture in .png format and visualized in the interface. The location information from the ROS was transferred to the said picture and the instant tracking of the AMR was done via the interface. It was shown which algorithm is used locally and globally at that moment. While in autonomous mode, the vehicle was provided to move to the previously recorded point. The total distance and time spent by the AMR while moving between two points were also calculated by the interface. The location (x, y, z) and orientations (x, y, z, w) of the previously recorded station were monitored from the stop list. On the other hand, the position (x, y, z) and orientation (x, y, z, w) information of the AMR could be followed as real time via the interface. In this way, when the AMR reaches the goal station, the time elapsed between two points, the transportation distance, settlement information such as the location and orientation of the vehicle can be tracked and be compared via the interface.

A Novel Approach for Target Attraction and Obstacle Avoidance of a Mobile Robot in Unknown Environments Using a Customized Spiking Neural Network

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications.

Development of Lower Computer for Hydraulically Actuated Quadruped Bionic Robots Based on DSP

Background: Hydraulic quadruped robots have broad application prospects. Control system design is the core content of robot design. However, the micro-controllers used in the past have shortcomings such as long sampling period and simple algorithm. Methods: An electric control system of the layered, distributed structure for hydraulically actuated quadruped robots is designed considering a dog as a bionic model. In order to improve the response time and the steady precision of the system at the same time, a Fuzzy–PID compound control algorithm is put forward in this paper. The hardware and software of the control system are designed. Results: The lower computer’s control system for hydraulically actuated quadruped robots is developed using TMS320F28335 in series of DSP2000 as the core processor. Outside control circuit expands some external chips, such as AD7606, AD5754R, and PCA82C250, and a peripheral interface circuit is designed. Taking full advantage of the efficient processing power of the chip and the rich on-chip resources, the hardware circuit is simpler and reliable, and the software is also easy to implement. It is verified that the control system is rational and effective using experiments. Conclusions: The experimental results show that the control system designed in this paper is reasonable and can effectively control the joints of the quadruped robot. It has strong scalability and can meet the basic requirements of the autonomous mobile robot control system.

Design of Bluetooth 5.1 Angle of Arrival Homing Controller for Autonomous Mobile Robot

With the improvement of autonomous robot navigation technologies, mobile robots can now be deployed in uncertain, real-world environments. An aspect of autonomous robot navigation in such scenarios is the capability to navigate to a real-time determined (previously unknown) location anywhere in its vicinity. This is especially pertinent for indoor navigation where existing localization technologies such as GPS do not provide sufficient accuracy of target location. In this paper, a controller design is proposed which homes a mobile robot to an object of unknown location using Bluetooth 5.1 Angle of Arrival (AoA) technology. The proposed setup consists of a target object with a Bluetooth beacon and a single Bluetooth antenna array mounted on a mobile robot. The controller uses a hybrid approach to calculating and updating the estimated target position by implementing parallax and vector position calculations from AoA and RSSI Bluetooth data. Simulations with various levels of sensor noise showed convergence to accurate target positions (mean accuracy of 0.12 m or less) in both obstacle-free and obstacle-present environments. The controller can be implemented as a standalone controller by directly commanding robot motion toward the target, or it can integrate with other existing robot navigation techniques by outputting a target position.

Trajectory control of autonomous mobile robots considering disturbance with machine learning agents

Trajectory control of autonomous mobile robots considering disturbance with machine learning agents

Intuitive and Flexible Process Control for Autonomous Mobile Robots: A Case Study in a Large Logistics Enterprise

Although the usage of autonomous mobile robots (AMRs) has many advantages, the implementation of AMRs is often hindered through expensive programming, unfavorable process requirements and insufficient operator interaction possibilities. In this paper, we present an analysis of the intralogistic processes in the technical centers of a large automotive logistics enterprise and discuss the challenges of AMR implementation. Based on this analysis and our previous research on an intuitive control software, we present a concept on how to implement flexible and intuitive process control for AMRs. This includes the definition of a visual programming interface, an extended human-machine-interface (HMI) that improves communication between operators and AMRs, and a planning tool that manages multiple AMRs and operators. The results of our process analysis in the selected enterprise show that the design of the shop floor, process flexibility and the task itself, which often requires human interaction with the AMR, are challenges that hinder the implementation of current AMRs. This is mostly attributed to the AMRs requirements of a mostly static environment and process structure. Especially ad hoc changes to the process that needs to be implemented immediately are often required and cannot be realized with usual AMR software. Therefore, our interface concept strongly couples the programmed AMR routines with our HMI on mobile devices that are carried by each operator. This way, we provide flexible interaction between AMR and operators. We further discuss how we plan to implement and evaluate our proposed concepts and tools within the presented enterprise.

Research on Risk Assessment in Cloud Control of Autonomous Mobile Robots

Research on Risk Assessment in Cloud Control of Autonomous Mobile Robots

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.

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

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.

Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation.

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.

Climbing control of autonomous mobile robot with estimation of wheel slip and wheel-ground contact angle

Climbing control of autonomous mobile robot with estimation of wheel slip and wheel-ground contact angle

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Numerous challenges are usually faced during the design and development of an autonomous mobile robot. Path planning and navigation are two significant areas in the control of autonomous mobile robots. The computation of odometry plays a major role in developing navigation systems. This research aims to develop an effective method for the computation of odometry using low-cost sensors, in the differential drive mobile robot. The controller acquires the localization of the robot and guides the path to reach the required target position using the calculated odometry and its created new two-dimensional mapping. The proposed method enables the determination of the global position of the robot through odometry calibration within the indoor and outdoor environment using Graphical Simulation software.

3D Environment Exploration with SLAM for Autonomous Mobile Robot Control

In the paper a solution for building of 3D map of unknown terrain for the purposes of control of wheeled autonomous mobile robots operating in an isolated and hard-access area is described. The work environment is represented by a three-dimensional occupancy grid map built with SLAM techniques using LIDAR sensor system. Probabilistic methods such as adaptive Monte Carlo localization and extended Kalman filter are used to concurrently build a map of surroundings and a robot’s pose estimation. A robot’s displacement and orientation are obtained from odometry and inertial navigation system. All algorithms and sub-systems have been implemented and verified with Robot Operation System with a framework for exploration tasks in multi-level buildings

An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications

The fourth industrial revolution, or Industry 4.0 (I4.0), makes use of wireless technologies together with other industrial Internet-of-Things (IIoT) technologies, cyber–physical systems (CPS), and edge computing to enable the optimization and the faster re-configuration of industrial production processes. As I4.0 deployments are ramping up, the practical integration of 5G wireless systems with existing industrial applications is being explored in both Industry and Academia, in order to find optimized strategies and to develop guidelines oriented towards ensuring the success of the industrial wireless digitalization process. This paper explores the challenges arisen from such integration between industrial systems and 5G wireless, and presents a framework applicable to achieve a structured and successful integration. The paper aims at describing the different aspects of the framework such as the application operational flow and its associated tools, developed based on analytical and experimental applied research methodologies. The applicability of the framework is illustrated by addressing the integration of 5G technology into a specific industrial use case: the control of autonomous mobile robots. The results indicate that 5G technology can be used for reliable fleet management control of autonomous mobile robots in industrial scenarios, and that 5G can support the migration of the on-board path planning intelligence to the edge-cloud.

A Hybrid Method for Industrial Robot Navigation

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.

5G Swarm Production: Advanced Industrial Manufacturing Concepts Enabled by Wireless Automation

This article presents an overview of current Industry 4.0 applied research topics, addressed from both the industrial production and wireless communication points of view. A roadmap toward achieving the more advanced industrial manufacturing visions and concepts, such as “swarm production” (nonlinear and fully decentralized production) is defined, highlighting relevant industrial use cases, their associated communication requirements, as well as the integrated technological wireless solutions applicable to each of them. Further, the article introduces the Aalborg University 5G Smart Production Lab, an industrial lab test environment specifically designed to prototype and demonstrate different Industrial IoT use cases enabled by the integration of robotics, edge-cloud platforms, and autonomous systems operated over wireless technologies such as 4G, 5G, and Wi-Fi. Wireless performance results from various operational trials are also presented for two use cases: wireless control of industrial production and wireless control of autonomous mobile robots.