Robot Navigation Method Research Articles

일반화된 보로노이 다이어그램을 이용한 논홀로노믹 모바일 로봇의 자율 주행

This paper proposes an autonomous navigation method for a nonholonomic mobile robot, based on the generalized Voronoi diagram (GVD). We define the look-ahead point for a given motion constraint to determine the direction of motion, which solves the problem of a minimum turning radius for the real nonholonomic mobile robot. This method can be used to direct the robot to explore an unknown environment and construct smooth feedback curves for the nonholonomic robot. As the trajectories can be smoothed, the position of the robot can be stabilized in the plane. The simulation results are presented to verify the performance of the proposed methods for the nonholonomic mobile robot. Furthermore, this approach is worth drawing on the experience of any other mobile robots.

移動ロボットの性能に応じた走行可能性推定が可能な不整地に対する走行可能性推定および行動生成手法

This paper describes a novel rough terrain traversability analysis and behavior generation method for mobile robot navigation. We focused on the scenario of mobile robot operation in complex environment such as disaster sites. The proposed method enables mobile robots to analyze terrain traversability of surrounding area and select safe course direction. According to the selected course direction, appropriate control input is calculated and mobile robot moves following the calculated control input. Terrain traversability analysis and course direction judgment are realized based on feature value calculation of terrain, fuzzy inference and vector field histogram. Mobile robot's behavior generation is realized based on control method which takes advantage of reference posture tracking. The experimental results show that the proposed method can navigate mobile robot successfully to the target location in complex environment including terrain area.

Mobile Robot Navigation based on Human Walking Trajectory in Intelligent Space

This paper presents a mobile robot navigation method based on human walking trajectory in intelligent space. Unlike the traditional navigation methods, the proposed method enables the robot to walk a meaningful path, which reflects human activity patterns in daily life. Human walking trajectories are extracted using multiple distributed vision cameras in intelligent space. To group trajectories with the same direction, trajectory similarity measurement is implemented. Key points on human walking trajectories and typical transfer points are utilized to build a topological map. To navigate a mobile robot to walk along the trajectory of human walking, global path planning based on the topological map and local path planning based on gird map are carried out with the spared information in intelligent space. Experimental results illustrate the performance of the proposed navigation method.

캠시프트와 KLT특징 추적 알고리즘을 융합한 모바일 로봇의 영상기반 사람추적 및 추종

Many mobile robot navigation methods utilize laser scanners, ultrasonic sensors, vision camera, and so on for detecting obstacles and path following. However, human utilizes only vision(e.g. eye) information for navigation. In this paper, we study a mobile robot control method based on only the camera vision. The Gaussian Mixture Model and a shadow removal technology are used to divide the foreground and the background from the camera image. The mobile robot uses a combined CAMSHIFT and KLT feature tracker algorithms based on the information of the foreground to follow a person. The algorithm is verified by experiments where a person is tracked and followed by a robot in a hallway.

Development of the Autonomous Mobile Robot for Target-Searching in Urban Areas in the Tsukuba Challenge 2013

This paper describes a navigation method for autonomous mobile robots and the knowledge obtained through trial runs conducted during the Tsukuba Challenge 2013 whose main tasks were autonomous navigation by robots to a goal and searching for target persons in several urban areas. Accurate maps are an important tool in localization on complex courses. We constructed occupancy grid map making method using laser scanners, gyro-assisted odometry and a DGPS. In trial runs, robots detect target parsons two ways – one involving color detection in images and the other involving laser scanner intensity data. The major problem with these methods is misdetection. To minimize this, we mask areas in which target persons should not exist on occupancy grid maps. Target candidates detected in masked areas are rejected, which indicates the possibility of using accurate occupancy grid maps as a user-friendly graphical interface. This paper focuses on the localization method, the target detection method and autonomous navigation knowledge in common space through the challenge.

Using Fuzzy Logic in Robot Navigation to Find Animals

 Fuzzy logic is a tool to use human expertise. The simplicity of fuzzy-rule based systems and its power to perform various tasks without accurate measurement and computation makes it very popular between sciences. One of these applications is using fuzzy logic in designing the controller for navigation of autonomous robots to move in various environments. This paper proposes a new method of robot navigation based on fuzzy logic. This method can be drawn upon to design robots which can find and catch different kind of animals, especially endangered species. It works based on a hierarchical set of behavior each of which acts by using a set of fuzzy rules. The proposed method is simulated and tested by MATLAB software.

Multi-granularity and metric spatial reasoning

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STARm, CDCm and OPRAm. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRAm. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRAm with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.

Vision Recognition on Line Pattern Following Behavior on POB-Bot

Robust development in the industries had lead to the fast development of the robots and sensors. Mobile robot is able to replace human power for many things due to its low cost and high accuracy. Types of navigation of the mobile robot had been in the research for past decades, the simplest and straight forward method of mobile robot navigation will be line following. Line following basically allows the robot to move along the predefined line. This paper is proposing a novel method of line following for more functions for mobile robot. 12 patterns have been defined to perform different functions. POB-Bot has been used to recognize these patterns using its POB-Eye. The result shows that the accuracy of the POB-Eye is promisingly high for simple pattern.

Intelligent Mobile Robot Navigation in an Uncertain Dynamic Environment

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in dynamic environment and avoids stationary and moving obstacles.

An Overview of Coal Mine Rescue Robot and its Navigation

After the coal-mine accident, to get information of the coal-mine accident has become the key of disaster rescue and relief, and after disaster, the environment is very bad, the rescue-workers cant arrive at the scene of the accident to obtain information at the first time, so the coal-mine rescue robot was brought into being, it can reach the place where the person hard to go to implement the detection of information and rescue. This paper introduces the research status of coal-mine rescue robot. According to the different methods of navigation, this paper explained the research progress of the methods for robot navigation, and made a summary and prospect for its potential applications and future research work.

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.

Path following behavior for an autonomous mobile robot using neuro-fuzzy controller

This paper presents a navigation method for an autonomous mobile robot. In order to equip the robot by capability of autonomy and intelligence in its environment, the control system must perform many complex information processing tasks in real time and it is well suited to use the soft-computing techniques. The objective of this paper is to elaborate an intelligent control system for the path following behavior by mobile robot using a neuro-fuzzy controller. The hybrid approach refers to the way of applying learning techniques offered by neural networks for fuzzy systems parameter identification. The proposed controller is used for pursuing a moving target. Simulation results show the effectiveness of the designed controller.

Path Planning and a Mobile Robot Navigation Method Based on a Human Frequency Map

Under the assumption that sensors are distributed in the environmental side to measure human movement in a term, such as a couple of days, we first propose a human frequency map (HFM), which is a grid map based on the observed human position and frequency in the term. Then, using such an HFM and the distributed sensor data, the possibility of encountering human and the width of passage in addition to the current position of human are taken into account in our new path planning method. The usefulness of the proposed path planning approach is demonstrated through simulations in dynamic environments, as well as actual experiments to show the realization of safe robot navigation.

Mobile Robot Navigation Based on Circle Recognition

This paper presents a navigation method for a mobile robot using a visual system. Circular marks with specific colors are used for marking the significant points of the mobile robot’s trajectory that it needs to pass. The colors of the used marks are signalizing the way of their bypassing with the mobile robot (from the left or right side). The mobile robot uses only one camera for the marks recognition task and it is able to determine its own relative position from the detected marks. The image processing and the mobile robot’s trajectory planning algorithm working in real-time are described in this paper.

Omni-Directional Vision System for Mobile Robot Using Structured Lights

Vision system can obtain rich environmental information, and vision is one of the most important sensing methods for mobile robot navigation and positioning. An omni-directional vision system using laser illumination is presented. The system can obtain the distance information of the obstacles around the mobile robot by only one image. The principle of the vision system is expounded; the mapping relationship between the image plane and the laser plane is given based on the analysis of the system model. The system benefits from the advantages of the omni-directional vision and the structured light vision, such as large ranged environmental information, high precision and robustness, etc. Experimental results of measurement and map construction verify the vision system.

Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method.

A Visual Navigation Method for Robot Based on a GOR and GPU Algorithm

A Visual Navigation Method for Robot Based on a GOR and GPU Algorithm

A Visual Navigation Method of Mobile Robot Using a Sketched Semantic Map

A new visual navigation method for a mobile robot is proposed in this paper. Its originality lies in integrating a sketched map with a semantic map together for the robot's navigation and in using unified tags to help recognize landmarks. In this sketched semantic map, the outline and semantic information of key referenced objects are used to represent themselves and a rough route for the robot's navigation is also sketched. Over the course of robot navigation along the route, and in order to easily recognize the referenced objects from the complex background, a kind of unified label is designed and pasted on the potential referenced objects in advance. A recognition method based on the Pseudo-Zernike Moment and the Normalized Moment of Inertia is used to compute the matching degree between the real-time image of the referenced object and its similar outline in a database. In addition, the odometer information is also fused so as to roughly localize the robot. Finally, through a series of experiments, the advantage and efficiency of the new navigation method with real-time dynamic obstacle avoidance are testified with the help of the imprecise real map and route.

Multi-robot collision-free navigation based on reciprocal orientation

In this paper, a new method for robot navigation in dynamic environments, called the reciprocal orientation algorithm, is introduced. This algorithm deals with the case in which each robot moves without direct communication with the other robots. The algorithm suggested in this paper uses the concept of reciprocal orientation which guarantees both smooth and collision-free robot trajectories. The algorithm is implemented in several simulation scenarios, some of them involving tens of robots. The deadlock problem, which occurs between two robots, has been solved by using an adequate deadlock resolution algorithm.

A model of ant navigation based on visual prediction

A model of visual navigation in ants is presented which is based on a simple network predicting the changes of a visual scene under translatory movements. The model contains two behavioral components: the acquisition of multiple snapshots in different orientations during a learning walk, and the selection of a movement direction by a scanning behavior where the ant searches through different headings. Both components fit with observations in experiments with desert ants. The model is in most aspects biologically plausible with respect to the equivalent neural networks, and it produces reliable homing behavior in a simulated environment with a complex random surface texture. The model is closely related to the algorithmic min-warping method for visual robot navigation which shows good homing performance in real-world environments.