Indoor Autonomous Mobile Robot Research Articles

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots.

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments From Occupancy Grid Maps

Identifying the environment’s structure, through detecting core components such as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable objects), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ) of 2D occupancy maps that may be cluttered and incomplete. ROSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> is tested in several real-world publicly available cluttered maps obtained in different conditions. The results show that it can robustly identify the environment structure in 2D occupancy maps suffering from clutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation, ROSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

Recognition and Grasping of Objects by Active Vision using Indoor Autonomous Mobile Robot

Recognition and Grasping of Objects by Active Vision using Indoor Autonomous Mobile Robot

Low-Cost Calibration of Matching Error between Lidar and Motor for a Rotating 2D Lidar

For a rotating 2D lidar, the inaccurate matching between the 2D lidar and the motor is an important error resource of the 3D point cloud, where the error is shown both in shape and attitude. Existing methods need to measure the angle position of the motor shaft in real time to synchronize the 2D lidar data and the motor shaft angle. However, the sensor used for measurement is usually expensive, which can increase the cost. Therefore, we propose a low-cost method to calibrate the matching error between the 2D lidar and the motor, without using an angular sensor. First, the sequence between the motor and the 2D lidar is optimized to eliminate the shape error of the 3D point cloud. Next, we eliminate the attitude error with uncertainty of the 3D point cloud by installing a triangular plate on the prototype. Finally, the Levenberg–Marquardt method is used to calibrate the installation error of the triangular plate. Experiments verified that the accuracy of our method can meet the requirements of the 3D mapping of indoor autonomous mobile robots. While we use a 2D lidar Hokuyo UST-10LX with an accuracy of ±40 mm in our prototype, we can limit the mapping error within ±50 mm when the distance is no more than 2.2996 m for a 1 s scan (mode 1), and we can limit the mapping error within ±50 mm at the measuring range 10 m for a 16 s scan (mode 7). Our method can reduce the cost while the accuracy is ensured, which can make a rotating 2D lidar cheaper.

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

A Navigation of an Indoor Autonomous Mobile Robot Based on Fuzzy Logic Method

With the continuous movement of mobile robots in the actual environment, it will encounter some obstacles inevitably. In order to make it better to overcome the uncertainty of the environment and solve the problem which the environment has no prior information, this paper does research on the indoor autonomous mobile robot navigation based on fuzzy logic method. Whether the environment has obstacles or not, this paper uses fuzzy logic for autonomous navigation, but also uses infrared sensors to avoid obstacles as far as possible. As the result, it not only improves the adaptability of the mobile robot for the dynamic environment, but also deals with all kinds of emergencies well, and it also has good robustness. Finally, the validity and feasibility of the method are proved by simulation.

A study and implementation of mapping and speech recognition techniques for an autonomous mobile robot based on ROS

Autonomous mobile robots work in close interaction with humans in environments such as homes, hospitals, public places and disaster areas. In autonomous mobile robots, the main constraints are safety, autonomy and efficiency in helping the humans. Given these constraints, developing the autonomous mobile robot technologies is a major challenge for both the industry and the research society. This paper work is about how an indoor autonomous mobile robot can work based on robot operating system and using Lidar and other sensors to create a map of an environment, and perform autonomous navigation with using capabilities like dynamic obstacle avoidance, speech recognition and video streaming. To achieve the above features, different algorithms like SLAM, AMCL, dynamic window approach algorithms, and CMU PocketSphinx speech recogniser are used. For video steaming, ROS web video server is used and the recorded video can be sent to a remote desktop system using ROS network.

A study and implementation of mapping and speech recognition techniques for an autonomous mobile robot based on ROS

Autonomous mobile robots work in close interaction with humans in environments such as homes, hospitals, public places and disaster areas. In autonomous mobile robots, the main constraints are safety, autonomy and efficiency in helping the humans. Given these constraints, developing the autonomous mobile robot technologies is a major challenge for both the industry and the research society. This paper work is about how an indoor autonomous mobile robot can work based on robot operating system and using Lidar and other sensors to create a map of an environment, and perform autonomous navigation with using capabilities like dynamic obstacle avoidance, speech recognition and video streaming. To achieve the above features, different algorithms like SLAM, AMCL, dynamic window approach algorithms, and CMU PocketSphinx speech recogniser are used. For video steaming, ROS web video server is used and the recorded video can be sent to a remote desktop system using ROS network.

Episodic non-Markov localization

Markov localization and its variants are widely used for mobile robot localization. These methods assume Markov independence of observations, implying that the observations can be entirely explained by a map. However, in real human environments, robots frequently make unexpected observations due to unmapped static objects like chairs and tables, and dynamic objects like humans. We therefore introduce Episodic non-Markov Localization (EnML), which reasons about the world as consisting of three classes of objects: long-term features corresponding to permanent mapped objects, short-term features corresponding to unmapped static objects, and dynamic features corresponding to unmapped moving objects. Long-term features are represented by a static map, while short-term features are detected and tracked in real-time. To reason about unexpected observations and their correlations across poses, we augment the Dynamic Bayesian Network for Markov localization to include varying edges and nodes, resulting in a novel Varying Graphical Network representation. The maximum likelihood estimate of the belief is incrementally computed by non-linear functional optimization. By detecting timesteps along the robot’s trajectory where unmapped observations prior to such time steps are unrelated to those afterwards, EnML limits the history of observations and pose estimates to “episodes” over which the belief is computed. We demonstrate EnML using different types of sensors including laser rangefinders and depth cameras, and over multiple datasets, comparing it with alternative approaches. We further include results of a team of indoor autonomous service mobile robots traversing hundreds of kilometers using EnML.

Landmark Finding Algorithms for Indoor Autonomous Mobile Robot Localization

This contribution is oriented to ways of computer vision algorithms for mobile robot localization in internal and external agricultural environment. The main aim of this work was to design, create, verify and evaluate speed and functionality of computer vision localization algorithm. An input colour camera data and depth data were captured by MS® Kinect sensor that was mounted on 6-wheel-drive mobile robot chassis. The design of the localization algorithm was focused to the most significant blobs and points (landmarks) on the colour picture. Actual coordinates of autonomous mobile robot were calculated out from measured distances (depth sensor) and calculated angles (RGB camera) with respect to landmark points. Time measurement script was used to compare the speed of landmark finding algorithm for localization in case of one and more landmarks on picture. The main source code was written in MS Visual studio C# programming language with Microsoft.Kinect.1.7.dll on Windows based PC. Algorithms described in this article were created for a future development of an autonomous agronomical mobile robot localization and control.

Landmark Finding Algorithms for Indoor Autonomous Mobile Robot Localization

This contribution is oriented to ways of computer vision algorithms for mobile robot localization in internal and external agricultural environment. The main aim of this work was to design, create, verify and evaluate speed and functionality of computer vision localization algorithm. An input colour camera data and depth data were captured by MS® Kinect sensor that was mounted on 6-wheel-drive mobile robot chassis. The design of the localization algorithm was focused to the most significant blobs and points (landmarks) on the colour picture. Actual coordinates of autonomous mobile robot were calculated out from measured distances (depth sensor) and calculated angles (RGB camera) with respect to landmark points. Time measurement script was used to compare the speed of landmark finding algorithm for localization in case of one and more landmarks on picture. The main source code was written in MS Visual studio C# programming language with Microsoft.Kinect.1.7.dll on Windows based PC. Algorithms described in this article were created for a future development of an autonomous agronomical mobile robot localization and control.

2A2-Q02 多数のRFIDリーダとICタグテキスタイルを用いた移動ロボットのための自己位置同定(移動ロボットの自己位置推定と地図構築)

Accurate self-localization system is necessary for a mobile robot to work in a home environment. Studies of a indoor autonomous mobile robot often use vision sensors and laser range finders. It is difficult to estimate the exact global location if the environment has number of places that have similar shape boundaries or small number of landmarks to localize. Furthermore, it tends to take a long time to recover the self-localization estimation if it goes wrong at once. Self-localization system based on reading RFID-tags on floor is robust against changes of lighting condition, obstacles, furniture and doors conditions in the environment. This paper proposes a self-localization method based on multiple RFID readers and dense IC tag textile and shows its validity in a real environment.

Behavior-based search of human by an autonomous indoor mobile robot in simulation

The paper focuses on the development of a behavior-based strategy for an indoor autonomous mobile robot to find an elderly person living alone in an unstructured home environment. A perception of presence of human being in different rooms at different times of the day is maintained. Based on this perception, the estimation of possible locations of a person is carried out using Markov decision process. The implemented methodology mainly focuses on the two criteria for searching the person, namely distance to the destination and the probability of finding the person at that location. It also takes into account the last known position of the human being for the final estimation of the possible location. The robot navigates autonomously to the desired location by planning a path and avoiding the obstacles in the way. Upon reaching the destination, the process of detecting the human being in the surrounding environment is carried out. In order to validate all aspects of the robotic behavior over a longer period of time, prior to be used in an elderly care environment, a simulated environment resembling the real home apartment has been created. To make the simulation more realistic, an animated human character has also been developed which walks in to different rooms and exhibit different postures during its movements. The promising results from the 3D simulation show the effectiveness of the developed methodology.

RFID based Intelligent Navigation Methodology of a Nonholonomic Indoor Autonomous Mobile Robot

RFID based Intelligent Navigation Methodology of a Nonholonomic Indoor Autonomous Mobile Robot

Improved global localization of an indoor mobile robot via fuzzy extended information filtering

SUMMARYGlobal localization of mobile robots has been well studied using the extended Kalman filter (EKF) method. This paper presents a fuzzy extended information filtering (FEIF) approach to improving global localization of an indoor autonomous mobile robot with ultrasonic and laser scanning measurements. A real-time FEIF algorithm is proposed to improve accuracy of static global pose estimation via multiple ultrasonic data. By fusing odometric, ultrasonic, and laser scanning data, a real-time FEIF-based pose tracking algorithm is developed to improve accuracy of the robot's continuous poses. Several experimental results are performed to confirm the efficacy of the proposed methods.

Matching radar and satellite images for ship trajectory estimation using the Hausdorff distance

A novel technique to obtain position, velocity and radar biases estimates of a ship is described by matching ship-borne radar images to geo-referenced satellite images. The matching is performed through the minimisation of the averaged partial Hausdorff distances between data points in each image. The minimisation rapidly yields robust geographical latitude and longitude position measurements, as well as ship heading and radar biases. The accuracy of the measurements is improved by feeding them into a Kalman filter that also allows estimates of the ship&apos;s velocity to be obtained. The method provides an alternative effective position sensor for GPS denied environments, which may also be employed for automatic radar calibration of bearing and range biases or for indoor autonomous mobile robot navigation.

Using Optical Flow on Contours in Estimating Depth and Motion for an Indoor Autonomous Mobile Robot

It is well known that the apparent motion generated by the projection on the image plane of points viewed by a moving camera can be used for determining relative depth, egomotion and even focal distance. Furthermore, motion estimates must be derived from the variation in time of the intensity patterns present on the image plane, being this a generally complex problem. One class of techniques known as short-term motion estimation or optical flow, present a computational advantage in the sense of employing direct local operations instead of symbolic processing. However, due to the well known aperture problem, motion estimates are poor in regions of the scene where texture is scarce. Since indoor scenarios are typically composed of large uniform areas separated by strong gradient contours, estimating motion at equally spaced locations of the image plane implies wasting a huge amount of computational power on results that will be discarded because of their low quality. One approach to this problem is to estimate motion at locations where it is known beforehand that texture is present. This implies being able to handle this new configuration in an efficient manner and providing algorithms that can cope with the new situation. This paper describes the implementation of a system capable of performing such a solution in real time and shows results obtained from using it for a prototype mobile robot constructed at our lab and called GATO (Guiado Autónomo para Transporte en Oficinas).

Real-time vision-based navigation and 3D depth estimation for an indoor autonomous mobile robot

A Transputer based artificial vision system that allows estimating a mobile robot's absolute position (navigation), it's motion parameters (egomotion) and a depth map of the sight of view is presented. Navigation is achieved by tracking expected features present in basic 3D CAD information of the environment. Egomotion and relative depth are estimated using optic flow calculated on closed contours of points in the scene presenting high spatio-temporal gradients and require no a-priori knowledge.

Map representation of a large in-door environment with path planning and navigation abilities for an autonomous mobile robot with its implementation on a real robot

In this research a complete development and implementation of a navigation system for an in-door autonomous mobile robot is presented. The constitution of the developed navigation system consists of: (1) An efficient hierarchical map representation method to model a large in-door environment. This method describes the significant features of an actual in-door world consisting of either a single building or a network of buildings connected through common bridges (a corridor like pass way), in which the robot's environment is divided hierarchically using a tree structure into three levels, Building, Corridor and Room. (2) A prior preparation algorithm to prepare the information necessary for path planning using the robot's environment model for the purpose to have fast and efficient path planning algorithms. (3) Fast and computationally efficient automated path planning strategy that generates collision free paths made of straight lines. This strategy includes two algorithms, (a) hierarchical planning for Building and Corridor levels, and (b) free space planning for a Room level. Consequently, geometrical and scene information along a generated path is abstracted to support real time actions while navigating the path. (4) A real time navigation algorithm to ensure real time control of robot's behavior, and to monitor the actual execution of the planned path in a real environment. For the purpose of having a reliable navigation system, a new real time language based on the Action Mode concept as a unit of robot's motion representation is developed. Using this language the sensor based mobile robot's behavior can be described. The developed navigation system is implemented and demonstrated on an actual mobile robot called Yamabico. M in a real in-door environment. The demonstration results show the effectiveness of the implemented navigation system with its proposed constitution.