Search

This paper presents an efficient online approach for complete coverage path planning of mobile robots in an unknown workspace based on online boustrophedon motion and an optimized backtracking mechanism. The presented approach first performs a single continuous boustrophedon motion until a critical point is reached. In order to completely cover the environment, next starting point is decided by using the accumulated knowledge of the environment map. An efficient backtracking technique based on proposed Two-way Proximity Search algorithm is used to plan a path from the critical point to the new starting point. Simulation results show the efficiency of proposed backtracking approach with improved total coverage time, coverage path length and memory requirements.

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.

This paper proposes a path planner for a humanoid robot to enhance its performance in terms of the human-robot interaction perspective. From the human point of view, the proposed method uses the time index that can generate a path that humans feel to be natural. In terms of the robot, the proposed method yields a waypoint-based path, the simplicity of which enables accurate tracking even for humanoid robots with complex dynamics. From an environmental perspective through which interactions occur, the proposed method can be easily expanded to a wide area. Overall, the proposed method can be described as a scalable path planner via waypoints with a time index for humanoid robots. Experiments have been conducted in test beds where the robot encounters unexpected exceptional situations. Throughout these trials, the robot successfully reached the goal location while iteratively replanning the path.

In this paper, a robust algorithm that discriminates various eye motions from the ElectroOculoGram (EOG) signals is proposed. Previous researches that use the EOG only focused on saccadic motions or blinks. However, we cover all eye motions including double/triple blinks and left/right winks. Furthermore, we suggest a novel method, which removes noises of the EOG, to increase the robustness of the discrimination. The method is called “an ideal velocity shape algorithm” which compares the real velocity of the EOG with an ideal velocity designed under a noise free assumption. This algorithm significantly reduces the effects of the noises and thus enhances the robustness. Detected eye motions are used for aHuman-Computer Interaction (HCI) between a person and a mobile robot. In the HCI, the person successfully controlled the robot for a target tracking and point stabilization.

In this paper, we propose a path planner for a robotic vacuum cleaner (RVC). In the design of the planner, we consider two main issues: (1) human-friendly path generation and (2) low computational load. First, we analyze how human move and suggest a hypothesis that human navigate in a way that minimizes the sum of muscle and brain energy. By imitating the human path, we propose a human- friendly path planner. Also, the designed planner requires a low amount of computations which not only extends the battery running time but also decreases the hardware cost of the RVC. Experimental results show that the proposed path gives a more favorable impression to customers than conventional paths.

A covariance matrix is a tool that expresses the odometry uncertainty of mobile robots. The covariance matrix is a key factor in various localization algorithms such as the Kalman filter or topological matching. However, it is not easy to acquire an accurate covariance matrix because the real states of robots are not known. Till now, few results on estimating the covariance matrix have been reported. Also, those aze not validated by experiments or do not reflect the real phenomena. In this paper, we propose a novel method which approximates the covariance matrix in a physically reasonable way. Extensive experiments validate that our method yields a covariance matrix which is accurate enough for practical uses.

The Generalized Voronoi Graph (GVG) has been effectively used for the SLAM and navigation of mobile robots. Since the mobile robot should perform the GVG-based SLAM and navigation in unstructured environments using inaccurate noisy sensors, it requires accurate data association and robust navigation techniques. In this paper, we suggest three techniques for a robust construction of the GVG. The first one is a robust node matching technique and the second one is an adaptable sensor area matching method for the robust exploration. Finally we propose a technique which is capable of distinguishing between closely located nodes. The simulation results show that the proposed algorithm can work successfully under 20% errors of odometry and range sensor in unstructured environment.

This paper proposes a shortest path generation scheme for point stabilization of the car-like robot. An implicit shortest point stabilization algorithm has been suggested conceptually by Vendittelli. As an extension, we present an algorithm which is more explicit and faster than previous one. Our scheme provides exact equations for practical implementation. Moreover, its computation time is remarkably lower than the previous one for the case in which the final point changes frequently. We provide a proof of the optimality and validate the performance of our algorithm through the simulation and experiment. where v(t) ,ω (t) are a linear and an angular velocity. These two velocities are control inputs to the robot. We assume minimum turning radius as 1 and |v(t )=1 | which means that the robot can go both forward and backward with constant speed. Now, we say(x(t0) ,y (t0) ,θ (t0)) as an initial posture and (x(tf) ,y (tf) ,θ (tf)) as a final posture, where t0 and tf are an initial and a final time. We also denote (xf ,y f) as a final point.

Observability analysis is a very powerful tool for discriminating whether a robot can estimate its own state. However, this method cannot investigate how much of the system is observable. This is a major problem from a state estimation perspective because there is too much noise in real environments. Therefore, although the system (or a mobile robot) is observable, it cannot estimate its own state. To address this problem, we propose an observability analysis method that uses the condition number. Mathematically, the condition number of matrix represents a degree of robustness to noise. We utilize this property of the condition number to investigate the degree of observability. In other words, the condition number of the observability matrix demonstrates the feasibility of state estimation and the robustness of its feasibility for estimation.

The main topic of this paper is to develop a new RGB-Depth sensor system with 2Dimensional Field of View (2D FoV) to build a 3D realistic map. Through analyzing pros and cons of existing sensors, we found to the requirements. They are 2D FoV, sensing more than 10 meters distance, eye-safe and eye-invisible. To configure the system to meet these conditions, emitter part and receptor part were configured by DLP projector equipped with DMD chip and cold mirror used for IR stereo respectively. The feasibility of this new sensor system was analyzed through experiments. Refereed/Peer-reviewed

Analysis on causes of inconsistency of Extended Kalman Filter (EKF) Simultaneous Localization And Mapping (SLAM) is mentioned in this paper. The effect of noise models and observation models on the inconsistent behavior of EKF SLAM was examined. In addition, the effect of registration of the false landmarks on inconsistent behavior of the EKF SLAM is mentioned in this paper. This paper identifies several causes of inconsistency of EKF SLAM by observability analysis.

Although there have been many researches on mobile robot localization, it is still difficult to obtain reliable localization performance in a human co-existing real environment. Reliability of localization is highly dependent upon developer's experiences because uncertainty is caused by a variety of reasons. We have developed a range sensor based integrated localization scheme for various indoor service robots. Through the experience, we found out that there are several significant experimental issues. In this paper, we provide useful solutions for following questions which are frequently faced with in practical applications: 1) How to design an observation likelihood model? 2) How to detect the localization failure? 3) How to recover from the localization failure? We present design guidelines of observation likelihood model. Localization failure detection and recovery schemes are presented by focusing on abrupt wheel slippage. Experiments were carried out in a typical office building environment. The proposed scheme to identify the localizer status is useful in practical environments. Moreover, the semi-global localization is a computationally efficient recovery scheme from localization failure. The results of experiments and analysis clearly present the usefulness of proposed solutions.

In this paper, we propose an algorithmic compass that yields the heading information of a mobile robot using the vanishing point in indoor environments: VPass. With the VPass, a loop-closing effect (which is a significant reduction of errors by revisiting a known place through a loop) can be achieved even for a loop-less environment. From the implementation point of view, the VPass is useful because it can be appended upon any existing navigation algorithms. Experimental results show that the VPass yields accurate angle information in indoor environments for paths with lengths of around 200m.

In this paper we propose a novel vision-based global localization method based on a hybrid map representation. We employ PCA-SIFT features as visual landmarks and represent the environment with a hybrid map which consists of a global topological map and local metric maps. To localize where a mobile robot is placed, we extract visual features from the currently captured view and match them to the feature database previously constructed according to the hybrid map representation. After filtering noise, we estimate the robotpsilas pose with the qualified matching features by the RANSAC approach. We implemented the proposed method in a real mobile robot and tested in both a home-like room and an office-like corridor. The experimental results show that our vision-based global localization system is acceptable in terms of processing time and accuracy.

This paper presents a practical algorithm for topological navigation in corridor environment using cheap sonar sensors. Topological navigation consists of topological mapping and obstacle avoidance navigation. In this environment, it has two major problems which are 1) identifying nodes under globally similar but locally slightly different shape of corridor environment for topological mapping and 2) obstacle avoidance navigation using cheap sensors. In order to detect nodes robustly, we define the Eigenvalue Ratio (EVR) which converts geometrical shape of the environment to a quantitative value based on the Principal Component Analysis (PCA). This method is demonstrated by simulation and experiments to be robust, with the notable detail that at a given corridor intersection the method can detect a cluster of nodes defined in this manner. For safe obstacle avoidance navigation, we introduce Circle Following (CF) algorithm which uses geometry of environment and characteristics of sonar sensors. Experiments and simulation validate reliability of the CF algorithm.

Edges in topological maps have rich information such as shape, numbers of obstacles, locations of obstacles which can be used for a data association. However, no systematic approach on using the edge data for the data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by 27-28 times for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations and experiments show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.

In this paper we propose a practical roadmap for mobile robots with differential wheel base. The roadmap is designed for a rectilinear environment which covers most of indoor buildings. In designing the roadmap, we put emphasis on two issues: (1) human friendly path and (2) low computation. First, we analyze how human move. From the human movement, we find out that human navigate in a way that minimizes the sum of a muscle and a brain energy. On the basis of the human path, we suggest an algorithm with low calculations. Experimental results show that the path generated by our method is more human-friendly and natural.

The data association, which matches a recent observation with known data set for the localization of mobile robots, has been realized by various sensor data. However, no data association technique which uses the edge data of topological maps has been reported, even though rich information is contained in the edge. This paper proposes a systematic way of utilizing the edge data for the data association. We explain the basic scheme and provide the market expectation.

In this paper, an algorithm for door detection using a cheap PC-camera is developed based on the Bayesian network in a hallway where doors are located at various depth. The open/close status of the door is also important in developing a GVG-SLAM and we extracted five important door features from the image and distance data. We applied the Bayesian network algorithm and an entropy concept to increase the robustness. Experiment result shows that the proposed algorithm can help the GVG-SLAM of mobile robot by successfully identifying the status of the door.

In this paper, an algorithm for hyper symmetric environment simultaneous localization and mapping (SLAM) is developed based on the generalized Voronoi graph. The hyper symmetric environment is a challenging environment for the SLAM and the most difficult problem on this is a data association. To redeem this problem, we propose three techniques. The first one is a breadth first node navigation algorithm which highly reduces the complexity of the data association. The second scheme is an odometry calibration method which converts untrustable odometry into reliable one. The third method is a multi layered data association scheme. We integrate above three techniques into one framework and propose a SLAM algorithm for the hyper symmetric environment. The simulation result shows that the proposed algorithm can successfully cover a large hyper symmetric environment under 20% odometry uncertainty.

GVG has been effectively used as a sensor based navigation tool using 360/spl deg/ sensor data. For mobile guide robot applications, however, we can only use 180/spl deg/ sensor data and the robustness of the navigation algorithm is critical for successful applications. For that purpose, the robot should be equipped with three capabilities. Those are 1) incremental GVG construction, 2) robust GVG navigation and 3) navigation strategy that just uses half of the sensor scan, i.e. 180/spl deg/. In this paper, we propose a GVG navigation algorithm that has above 3 capabilities. We firstly propose a method that can estimate the invisible 180/spl deg/ range from previous range data. Moreover, we suggest a way of robust GVG navigation algorithm by using a sensor data matching technique. The simulation result validates that the proposed algorithm can incrementally and robustly navigate the semi-unstructured map by using 180/spl deg/ sensor scan.

All mobile robots suffer from odometry error. Relative localization from odometry has both the systematic and the non-systematic errors. However, once a precise system error model and its parameters are given, the accuracy of odometry can be remarkably improved. Most previous works on this effort focused on the differential drive robots with little attention to the other types of mobile bases. In this paper, we analyze sources of odometry error and propose an error model for the synchro drive robot. We then describe a novel procedure to accurately estimate the error parameters of the derived error model and the covariance matrix of the synchro drive robot. However, this procedure is general for all mobile bases, so we also apply our method for the differential drive robots and show experiments. This new process uses the shape of the path, as opposed to just end points, to estimate the error parameters and covariance matrix. We happen to use the generalized Voronoi graph to generate this path. Experimental results validate the error model of the synchro drive robot and precise estimation ability of the proposed method for the synchro and the differential drive robots.

This paper present the development of the human-mobile communication system using Electro Oculo Gram(EOG) signals. An “Ideal Velocity Shape” signal processing algorithm is proposed to extract position data wh ere the eyes are focusing on from the noisy and drift included EOG signals. Additionally, an efficient algorithm for the detection of various eye-lip movements such as blink and wink is suggested. Two experiments were performed for the validation of human-mobile communication system. One is point stabilization of mobile robot, using extracted eye focusing position data. The other is moving target following experiment using various eye-lip movements as mobile robot's command.