Autonomous Mobile Robot Navigation Research Articles

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

Bio-Inspired Autonomous Navigation and Formation Controller for Differential Mobile Robots.

This article proposes a decentralized controller for differential mobile robots, providing autonomous navigation and obstacle avoidance by enforcing a formation toward trajectory tracking. The control system relies on dynamic modeling, which integrates evasion forces from obstacles, formation forces, and path-following forces. The resulting control loop can be seen as a dynamic extension of the kinematic model for the differential mobile robot, producing linear and angular velocities fed to the mobile robot's kinematic model and thus passed to the low-level wheel controller. Using the Lyapunov method, the closed-loop stability is proven for the non-collision case. Experimental and simulated results that support the stability analysis and the performance of the proposed controller are shown.

Development of an Omnidirectional AGV by Applying ORB-SLAM for Navigation Under ROS Framework

The development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework is presented in this paper. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning using the oriented features from accelerated segment test and rotated binary robust independent elementary feature detector-simultaneous localisation and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realise time optimisation for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Further, the results showed a superior performance over the adaptive Monte Carlo localisation for robot localisation and dynamic window approach for local path planning.

Autonomous Navigation System of Indoor Mobile Robots Using 2D Lidar

Significant developments have been made in the navigation of autonomous mobile robots within indoor environments; however, there still remain challenges in the face of poor map construction accuracy and suboptimal path planning, which limit the practical applications of such robots. To solve these challenges, an enhanced Rao Blackwell Particle Filter (RBPF-SLAM) algorithm, called Lidar-based RBPF-SLAM (LRBPF-SLAM), is proposed. In LRBPF, the adjacent bit poses difference data from the 2D Lidar sensor which is used to replace the odometer data in the proposed distribution function, overcoming the vulnerability of the proposed distribution function to environmental disturbances, and thus enabling more accurate pose estimation of the robot. Additionally, a probabilistic guided search-based path planning algorithm, gravitation bidirectional rapidly exploring random tree (GBI-RRT), is also proposed, which incorporates a target bias sampling to efficiently guide nodes toward the goal and reduce ineffective searches. Finally, to further improve the efficiency of navigation, a path reorganization strategy aiming at eliminating low-quality nodes and improving the path curvature of the path is proposed. To validate the effectiveness of the proposed method, the improved algorithm is integrated into a mobile robot based on a ROS system and evaluated in simulations and field experiments. The results show that LRBPF-SLAM and GBI-RRT perform superior to the existing algorithms in various indoor environments.

A Modified Dijkstra Algorithm for ROS Based Autonomous Mobile Robots

Autonomous Mobile Robots (AMRs) are frequently used in many fields of technology. In this study, an AMR was designed to execute different path planning algorithms. Firstly, working principle, system architecture and motion planning of AMR are presented. Then, a map for the current environment is produced by a Robot Operating System (ROS) powered AMR which was designed for this study. The AMR locates itself on the produced map with the aid of an integrated Light Detection and Ranging sensor (LIDAR). The locomotion of AMR to a user-defined target on the produced map is performed by an optimal path based on AMR's own navigation plan. Two different path planning algorithms, which are Dijkstra’s algorithm and a modified version of Dijkstra’s algorithm, are executed on a cost-effective AMR platform, which has the capability of Simultaneous Localization and Mapping (SLAM). The reason why Dijkstra algorithm is handled in this study rather than A*, D* and RRT algorithms is that this algorithm is a basic and widely used algorithm. Dijkstra’s algorithm is modified, and pros and cons of the modified algorithm are analysed compared to Dijkstra algorithm. The proposed algorithm and navigation of AMR are tested both in real time in real world and as a simulation in Gazebo. Two algorithms were compared according to the results obtained from the robot locomotion both in real application and simulation environment. It is observed that the modified version of the Dijkstra’s algorithm comparatively yielded a bit more satisfactory results in the aspect of path planning.

An Obstacle-Finding Approach for Autonomous Mobile Robots Using 2D LiDAR Data

Obstacle detection is crucial for the navigation of autonomous mobile robots: it is necessary to ensure their presence as accurately as possible and find their position relative to the robot. Autonomous mobile robots for indoor navigation purposes use several special sensors for various tasks. One such study is localizing the robot in space. In most cases, the LiDAR sensor is employed to solve this problem. In addition, the data from this sensor are critical, as the sensor is directly related to the distance of objects and obstacles surrounding the robot, so LiDAR data can be used for detection. This article is devoted to developing an obstacle detection algorithm based on 2D LiDAR sensor data. We propose a parallelization method to speed up this algorithm while processing big data. The result is an algorithm that finds obstacles and objects with high accuracy and speed: it receives a set of points from the sensor and data about the robot’s movements. It outputs a set of line segments, where each group of such line segments describes an object. The two proposed metrics assessed accuracy, and both averages are high: 86% and 91% for the first and second metrics, respectively. The proposed method is flexible enough to optimize it for a specific configuration of the LiDAR sensor. Four hyperparameters are experimentally found for a given sensor configuration to maximize the correspondence between real and found objects. The work of the proposed algorithm has been carefully tested on simulated and actual data. The authors also investigated the relationship between the selected hyperparameters’ values and the algorithm’s efficiency. Potential applications, limitations, and opportunities for future research are discussed.

Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation

In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field (APF) algorithms. First, a TB3 Waffle Pi DQN is built and trained to reach the goal. Then the APF shortest path algorithm is incorporated into the DQN algorithm. The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System (ROS). The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algorithm. The performance improvement rate of the proposed DQN + APF in comparison with DQN in terms of the number of successful targets is attained by 88%. The performance of the proposed DQN + APF in comparison with DQN in terms of average time is achieved by 0.331 s. The performance of the proposed DQN + APF in comparison with DQN average rewards in which the positive goal is attained by 85% and the negative goal is attained by −90%.

Acquisition of Action Models for Autonomous Navigation of Mobile Robots Using Deep Reinforcement Learning Based on Sharing Positions in Robot Network

Acquisition of Action Models for Autonomous Navigation of Mobile Robots Using Deep Reinforcement Learning Based on Sharing Positions in Robot Network

Observation Model for Robust Localization of Mobile Robots based on Particle Filter in Changing Environments

For autonomous mobile robot navigation, localization is an essential capability. Given a mobile robot equipped with a 3D LiDAR sensor, an environment map composed of point cloud is built beforehand. The robot is thus allowed to localize the position in the map using the sensor scan data. However, the environment sometimes changes due to obstacles. Under the changing environment, the localization capability of the robot might be decreased. For this challenge, we propose a sensor observation model in a framework of particle filter based localization. In the observation model, we focus on the distance and distribution of point clouds of the map and sensor scan data. In the experiments, a mobile robot is moved by an operator in a virtual environment with obstacles. The robot based on the proposed observation model is able to localize the position in both the original and changing environments with the same accuracy. From the results, we finally show the robustness of the localization capability for changing environments.

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%.

Monocular vision with deep neural networks for autonomous mobile robots navigation

Enabling mobile robots to explore the formerly unidentified environment is a challenging task. The current paper describes the internal analysis algorithm for mobile robots that combines various convolutional neural network (CNN) layers with the decision-making process in a hierarchical way. The whole system is trained end-to-end on data captured by a low-cost depth camera (RGB-D). The output consists of the proposed expansion model of the robot's critical moving directions to achieve autonomous analysis ability. Training this model through the dataset is created using Hand-Controlled Mobile Robot (HCMR) built for this purpose. The experiments were conducted by moving this robot in natural and diverse environments. The robot was trained using this data and applied for environmental investigation decisions (the control labels) using CNN to enable the robot to automatically sense the navigation without a map in an unknown environment. Furthermore, extensive experiments were conducted indoors and attained an accuracy of 77%. Experiments showed that the proposed model was able to reach equivalent results that are generally obtained enormously from an expensive sensor. In addition, comprehensive comparisons were drawn between the human-controlled robot and a robot trained using a deep learning process to determine decisions to control the robot's movement. The reached results were identical and satisfactory.

P‐16.12: Simultaneous Localization and mapping technology based on solid‐state lidar

The problems of autonomous navigation include positioning, mapping and path planning. SLAM has become one of the most popular and mature solutions for autonomous navigation of mobile robots. SLAM literally translates to locating and mapping simultaneously. In general, SLAM frameworks use cameras, IMU, wheel encoders, and a combination of light detection and ranging (lidar) sensors to provide superior accuracy for navigation systems. However, the price of mechanical lidar is exceptionally high compared to other sensors, which is the main reason for introducing a new generation of lasers called solid State Lidar (SSL)

Robot Navigation Based on Situational Awareness

Autonomous navigation for mobile robots is a prerequisite to complete the tasks. In this article, we propose a novel navigation method based on situational awareness. The scene predictor, scene interpreter and the topological map-based planner are the main components, where the scene interpreter is used to realize the mapping of perception information, and the scene predictor predicts the information in the neighborhood of current position. The basic idea of this research is the feeling of path. Based on the definitions of path and the amount of perception, the situational awareness fitting network is designed as the scene interpreter, and the position of the robot in a path can be implicitly described by a situational awareness value. With the global guidance of the planner, the robot achieves navigation. The proposed navigation method does not rely on Cartesian-based global position and its effectiveness is verified by simulations and experiments.

Spatial Expression of Multifaceted Soft Decoration Elements: Application of 3D Reconstruction Algorithm in Soft Decoration and Furnishing Design of Office Space

In China’s modern market economy under the rapid development of the general situation, we work more and more problems, and work pressure is also increasing. The so-called office space refers to the space layout, style, and the physical and psychological division of the space. Office space must take into account many factors, involving technology, technology, humanities, aesthetics, and other elements, while the office space is the space where people work and relax. In recent years, as people’s requirements for the work environment are increasingly high, therefore, the design of the office space is also more and more attention to people. The concept of soft furnishing design into the work space will help improve the overall corporate and office space design of cultural taste which is one of the main methods to show the quality and human connotation of the enterprise. The three-dimensional reconstruction refers to the creation of a mathematical model suitable for computer display and processing of three-dimensional space objects. It is an important basic tool for data processing, computing, and researching the performance of mathematical models in the computer environment, which can be applied in various fields such as autonomous navigation of mobile robots, aviation and remote sensing computing, industrial monitoring information system, medical imaging, and virtual reality. The 3D environment reconstruction technology has become one of the popular research areas in computer vision and increasingly attracts the attention of design practitioners. This paper takes the 3D environment reconstruction technology of office space soft decoration design as the basis and discusses the important elements and modeling ideas in soft decoration design, which adds to the interior design of office space, and uses Kinect to obtain the depth data in the 3D environment, so as to complete the realistic 3D reproduction of the interior environment based on computer vision technology.

Autonomous navigation of mobile robot in radiation environment with uneven terrain

Autonomous navigation of mobile robot in radiation environment with uneven terrain

Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm

For mobile robot autonomous navigation, path planning algorithm matters. For the reason that traditional A* algorithm is unable to achieve the global path planning perfectly and avoid unknown obstacles at the same time, the mobile robot algorithm combining improved A* with adaptive DWA was proposed for path planning. Firstly, safe distance judgment is added in A* algorithm to avoid the problem that the path crosses the vertex of obstacle diagonally. Secondly, the dynamic tangential method is used to smooth the path of A* algorithm, which is more consistent with the actual motion curve of mobile robot. Then, the path key points explored by improved A* algorithm are extracted as intermediate target points of the velocity adaptive DWA algorithm. Unknown obstacles avoidance based on the optimal global path planning is realized. Finally, the proposed fusion algorithm’s feasibility is verified through mobile robot ROS simulation.

The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)

The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM.

EKF-LOAM: An Adaptive Fusion of LiDAR SLAM With Wheel Odometry and Inertial Data for Confined Spaces With Few Geometric Features

A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> , which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.

Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.