Mobile Robot Navigation System Research Articles

Indoor Mobile Robot Path Planning and Navigation System Based on Deep Reinforcement Learning

Indoor Mobile Robot Path Planning and Navigation System Based on Deep Reinforcement Learning

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.

Navigation System of a Differential-drive Wheeled Mobile Robot Using a GA-Tuned Kinematic Controller

Navigation System of a Differential-drive Wheeled Mobile Robot Using a GA-Tuned Kinematic Controller

Survey on Embedding Economical Autonomous Navigation System for Mobile Robots and UAV

Navigation has traditionally served the purpose of determining one's position, locating destinations, and charting a course towards them. It furnishes accurate details about the whereabouts of specific places or objects. Despite numerous advancements and enhancements in navigation technology, there have been ongoing discussions about its potential for autonomy. This suggests a scenario where navigation operates independently, without human intervention. Devices equipped with this capability comprehend their destination and chart the most efficient route to reach it. A crucial concept in this context is Visual Odometry (VO), which calculates the relative position between successive image frames. Likewise, the positioning of mobile robots relies on similar principles. However, a significant challenge arises over time as VO is susceptible to accumulating errors, known as drift. The Inertial Measurement Unit (IMU), which consists of accelerometers, gyroscopes, and magnetometers, is added to counteract this. These elements provide data that is more accurate and helps reduce noise. The integration of IMU with VO results in the creation of Visual Inertial Odometry (VIO). Furthermore, combining VIO with Global Positioning System (GPS) data through an Extended Kalman Filter (EKF) enhances localization accuracy both locally and globally. Additionally, stereo disparity estimation is employed to generate a depth perception map for obstacle detection, converted into a 2D grid map of occupancy after. While a waypoint follower directs the robot or devices toward its intended objective, local route planning algorithms create interim waypoints to prevent obstructions.

Intelligent Enhanced Mobile Robotics Navigation: Integrating Neural Networks with Type-2 Fuzzy Logic for Dynamic Environments

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications.

Robot navigation based on multi-sensor fusion

Over the past years, with the rapid development of artificial intelligence technology, mobile robots have appeared in more and more fields. Different types of robots play different roles. Due to the complex indoor environment, there are still many technical problems to be solved for robots. Aiming at the shortcomings of single sensor, low mileage accuracy, and poor security in traditional mobile robot navigation systems, we propose a mobile robot autonomous navigation system based on multi-sensor perception. This system integrates mainstream synchronous positioning and mapping algorithms, path planning algorithms, and positioning algorithms, and uses multiple sensors to compensate each other to achieve the automatic navigation function of the mobile robot. In order to verify the reliability of the method, We have carried out multi angle analysis in the simulation environment, and the experimental results show that the proposed navigation system is reliable in operation, high in mileage accuracy, and good in robustness, which broadens the application scenario of mobile robots.

Advanced System for Enhancing Location Identification through Human Pose and Object Detection

Location identification is a fundamental aspect of advanced mobile robot navigation systems, as it enables establishing meaningful connections between objects, spaces, and actions. Understanding human actions and accurately recognizing their corresponding poses play pivotal roles in this context. In this paper, we present an observation-based approach that seamlessly integrates object detection algorithms, human pose detection, and machine learning techniques to effectively learn and recognize human actions in household settings. Our method entails training machine learning models to identify the common actions, utilizing a dataset derived from the interaction between human pose and object detection. To validate our approach, we assess its effectiveness using a diverse dataset encompassing typical household actions. The results demonstrate a significant improvement over existing techniques, with our method achieving an accuracy of over 95% in classifying eight different actions within household environments.. Furthermore, we ascertain the robustness of our approach through rigorous testing in real-world environments, demonstrating its ability to perform well despite the various challenges of data collection in such settings. The implications of our method for robotic applications are significant, as a comprehensive understanding of human actions is essential for tasks such as semantic navigation. Moreover, our findings unveil promising opportunities for future research, as our approach can be extended to learn and recognize a wide range of other human actions. This perspective, which highlights the potential leverage of these techniques, provides an encouraging path for future investigations in this field.

Learning Observation Model for Factor Graph Based-State Estimation Using Intrinsic Sensors

The navigation system of autonomous mobile robots has appeared challenging when using exteroceptive sensors such as cameras, LiDARs, and radars in textureless and structureless environments. This paper presents a robust state estimation system for holonomic mobile robots using intrinsic sensors based on adaptive factor graph optimization in the degradation scenarios. In particular, the neural networks are employed to learn the observation and noise model using only IMU sensor and wheel encoder data. Investigating the learning model for the holonomic mobile robot is discussed with various neural network architectures. We also explore the neural networks that are far more powerful and have cheaper computing power when using the inertial-wheel encoder sensors. Furthermore, we employ an industrial holonomic robot platform equipped with multiple LiDARs, cameras, IMU, and wheel encoders to conduct the experiments and create the ground truth without a bulky motion capture system. The collected datasets are then implemented to train the neural networks. Finally, the experimental evaluation presents that our solution provides better accuracy and real-time performance than other solutions. <i>Note to Practitioners</i>&#x2014;Autonomous mobile robots need to serve in challenging environments robustly that deny extrinsic sensors such as cameras, LiDARs, and radars. In order to operate in this situation, the navigation system shall rely on the intrinsic sensor as inertial sensor and wheel encoders. Existing conventional methods have combined the intrinsic sensors in the form of the recursive Bayesian filtering technique without adapting these models. Besides, deep learning-based solutions have adopted extensive networks like LSTM or CNN to handle the estimation problem. This work aims to develop a state estimation subsystem of the navigation system for the holonomic mobile robots that utilize the intrinsic sensors in adaptive factor graph optimization. In particular, we present how to join the factor graph efficiently with the learning observation model of IMU and wheel encoder factor. Moreover, the neural networks are introduced to learn the observation model with an IMU and wheel encoder data inputs. We recognize that lightweight neural networks can achieve more power than deep learning techniques using the IMU sensor and wheel encoders. Finally, the neural networks are embedded in a factor graph to handle the smoothing state estimation. The proposed system could operate with high accuracy in real time.

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

A Scalable Tree Based Path Planning for a Service Robot

Path planning plays a vital role in a mobile robot navigation system. It essentially generates a shortest traversable path between two given points. There are many path planning algorithms proposed by researchers all over the world, however, there are very little work focussing on path planning for a service environment. The general assumption are either the environment is fully known or unknown. Both the cases will not be suitable for a service environment. Afully known environment will restrict further expansion in terms of number of navigation points and unknown environment would give an inefficient path. Unlike other environments, service environments have certain factors to be considered, like user friendliness, repeatability, scalability and portability which are very essential for a service robot. In this paper a simple, efficient, robust and environment independent path planning algorithm for an indoor mobile servicerobot is presented. Initially the robot is trained to navigate to all the possible destinations sequentially with minimal user interface which will ensure the robot knows partial paths in the environment. With the trained data the path planning algorithm maps all the logical paths between all the destinations, which helps in autonomous navigation. The algorithm is implemented and tested using 2D simulator Player/Stage. The proposed system is tested with two differentservice environment layouts and proved to have features like scalability, trainability, accuracy and repeatability. The algorithm is compared with various classical path planning algorithms and the results shows that proposed path planning algorithm is in par with the other algorithms in terms of accuracy and efficient path generation.

Behavior-based Navigation System for Mobile Robot in Greenhouse using 2D LiDAR

Behavior-based Navigation System for Mobile Robot in Greenhouse using 2D LiDAR

Mobile Robot Navigation Using Deep Reinforcement Learning

Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm. The simulation results show that the mobile robot can autonomously navigate, recognize, and reach the target object location in an unknown environment without colliding with static and dynamic obstacles. Similar results are obtained in real-world experiments, but only with static obstacles. The DDQN agent outperforms the DQN agent in reaching the target object location in the test simulation by 5.06%.

Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model

ABSTRAK Membangun sebuah sistem navigasi pada mobile robot yang bergerak di ruang sosial perlu memperhatikan beberapa aspek krusial, seperti menghindari rintangan, menjaga arah hadap robot ke tujuan, dan mencapai tujuan dengan cepat. Penelitian ini bertujuan untuk mengembangkan sistem navigasi pada Omnidirectional mobile robot menggunakan Fuzzy-Social Force Model (FSFM). Social Force Model (SFM) mampu menggerakan robot ke tujuan sambil menghindari rintangan. Fuzzy Inference System (FIS) digunakan untuk menghasilkan gain adaptif sebagai salah satu parameter SFM agar respon SFM sesuai dengan masukan dari sensor lidar. Aturan FIS dioptimasi agar mendapatkan nilai optimal menggunakan Particle Swarm Optimization (PSO). Dari hasil percobaan, mobile robot mencapai tujuan lebih cepat dengan selisih 1.59 s dan nilai error heading robot lebih kecil 0.9261 dibandingkan FSFM tanpa optimasi. Kata kunci: Sistem Navigasi, Mobile Robot, Fuzzy-Social Force Model, Optimasi, Particle Swarm Optimization ABSTRACT Building a navigation system on a mobile robot moves in social space needs to consider several crucial aspects, such as avoiding obstacles, keeping the robot facing the destination, and reaching the destination quickly. This study aims to develop a navigation system on an Omnidirectional mobile robot using the Fuzzy-Social Force Model (FSFM). The Social Force Model (SFM) guides the mobile robot to its destination while avoiding obstacles. The Fuzzy Inference System (FIS) produces adaptive gain as one of the SFM parameters so that the response of the SFM matches the data of the lidar sensor. The rule base of FIS is optimized to get the optimal value using Particle Swarm Optimization (PSO). From the experimental results, mobile robots reach the destination faster with a difference of 1.59 s and a minor error in robot heading of 0.9261 compared to FSFM without optimization. Keywords: Navigation System, Mobile Robot, Fuzzy-Social Force Model, Optimization, Particle Swarm Optimization

Implementasi Particle Swarm Optimization untuk Optimasi Fuzzy-Social Force Model pada Sistem Navigasi Robot Omnidirectional

Particle Swarm Optimization (PSO) is a swarm-based optimization method that is easy to implement and requires only a few parameters to set. This study aims to implement PSO to optimize the Fuzzy-Social Force Model (FSFM). FSFM combines the Social Force Model (SFM) as a navigation algorithm and the Fuzzy Inference Rule (FIS) to produce adaptive gain on SFM to create a mobile robot navigation system that is more responsive to obstacles. The PSO implementation optimizes fuzzy rules to be more optimal when the mobile robot navigates into social spaces. From the experimental test results on the VREP simulation software, cognitive parameter c1 = 1 and social parameter c2 = 2 produced the best navigation performance compared to other test parameter values.

Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Clock and Power-Induced Bias Correction for UWB Time-of-Flight Measurements

Ultra-Wide Band (UWB) communication systems can be used to design low cost, power efficient and precise navigation systems for mobile robots, by measuring the Time of Flight (ToF) of messages traveling between on-board UWB transceivers to infer their locations. Theoretically, decimeter level positioning accuracy or better should be achievable, at least in benign propagation environments where Line-of-Sight (LoS) between the transceivers can be maintained. Yet, in practice, even in such favorable conditions, one often observes significant systematic errors (bias) in the ToF measurements, depending for example on the hardware configuration and relative poses between robots. This letter proposes a ToF error model that includes a standard transceiver clock offset term and an additional term that varies with the received signal power (RxP). We show experimentally that, after fine correction of the clock offset term using clock skew measurements available on modern UWB hardware, much of the remaining pose dependent error in LoS measurements can be captured by the (appropriately defined) RxP-dependent term. This leads us to propose a simple bias compensation scheme that only requires on-board measurements (clock skew and RxP) to remove most of the observed bias in LoS ToF measurements and reliably achieve cm-level ranging accuracy. Because the calibrated ToF bias model does not depend on any extrinsic information such as receiver distances or poses, it can be applied before any additional error correction scheme that requires more information about the robots and their environment.

AN INTEGRATED AVOIDING AND APPROACHING HUMAN SYSTEMS FOR MOBILE SERVICE ROBOTS IN DYNAMIC SOCIAL ENVIRONMENTS

In this study, we present a socially aware navigation framework for mobile service robots by integrating the systems proposed in our previous studies into a completed mobile robot navigation system, including the human detection and tracking system that is used to detect and track humans; the encoder and laser-based localization and mapping system that is used to determine the position of the robot in the environment; the approaching pose estimation system that is used to estimate the approaching pose of the robot to the human; and the social timed elastic band-based motion planning system that is used to socially navigate the mobile robots to approach the human. In addition, in the paper we describe in detail the motor control system and the design of our mobile robot platform, which is then utilized to conduct experiments in the real-world environments. We verify the feasibility and usefulness of the proposed socially aware robot navigation framework through a series of experiments in a corridor-like environment. The experimental results show that our proposed framework is able to drive the mobile robots to both avoid and approach the humans, providing the safety and comfort for the humans and socially acceptable behaviors for the mobile service robots in the dynamic social environments.

Laser Based Navigation in Asymmetry and Complex Environment

For collision-free navigation in unstructured and cluttered environments, deep reinforcement learning (DRL) has gained extensive successes for being capable of adapting to new environments without much human effort. However, due to its asymmetry, the problems related to its lack of data efficiency and robustness remain as challenges. In this paper, we present a new laser-based navigation system for mobile robots, which combines a global planner with reinforcement learning-based local trajectory re-planning. The proposed method uses Proximal Policy Optimization to learn an efficient and robust local planning policy with asynchronous data generation and training. Extensive experiments have been presented, showing that the proposed system achieves better performance than previous methods including end-to-end DRL, and it can improve the asymmetrical performance. Our analysis show that the proposed method can efficiently avoid deadlock points and achieves a higher success rate. Moreover, we show that our system can generalize to unseen environments and obstacles with only a few shots. The model enables the warehouse to realize automatic management through intelligent sorting and handling, and it is suitable for various customized application scenarios.

Topographic SLAM Using a Single Terrain Altimeter in GNSS-Restricted Environment

In a Global Navigation Satellite System (GNSS)-restricted area, a mobile robot navigation system exploits surrounding environment information. For an aerial or underwater vehicle, undulating terrain of a land or seabed surface is a valuable information resource that leads to the development of terrain-referenced navigation (TRN) algorithms. However, due to the vast amount of a vehicle’s activity area, surveying all the regions to obtain a high-resolution terrain map is impractical and requires simultaneous localization and mapping (SLAM) as a highly desirable capability. This paper presents a topographic SLAM algorithm using only a single terrain altimeter, which is low-cost, computationally efficient, and sufficiently stable for long-term operation. The proposed rectangular panel map structure and update method enable robust and efficient SLAM. As terrain elevation changes are inherently nonlinear, an extended Kalman filter (EKF)-based SLAM filter is adopted. The feasibility and validity of the proposed algorithm are demonstrated through simulations using terrain elevation data from a real-world undersea environment.