基于最小边界扇形的移动对象轨迹实时化简算法

为了对全球定位系统(GPS)设备采集到的移动对象原始轨迹数据进行简化,提高轨迹数据的使用效率,降低移动终端的通信代价和计算开销,提出了一种基于最小边界扇形(MBS)的移动对象轨迹实时化简算法。该算法不同于用一条折线来近似原始轨迹的方法,它利用扇形预测范围来估计、简化原始轨迹,在角度和距离两个层面上对简化误差进行控制。提出了新的误差度量方法--基于等极径的误差度量方法,并讨论了GPS定位误差对简化算法的影响。实验结果表明,所提算法的简化轨迹高效、稳定,所得到的简化轨迹与原始轨迹之间误差较小(不超过误差阈值的20%),对GPS定位误差有较好的容错能力。     

Adaptive guaranteed estimation based on information provided by a set of “Relative” objects

Estimation algorithms for the state of an object based on “relative” objects information are proposed, under conditions of disturbances affecting the object and errors caused by incomplete and inaccurate measurements of the object’s vector. Information on disturbances and measurement errors is considered known up to some given sets. If the path of the object’s motion is changed, it is suggested to use information provided by a group of similar objects for identifying model of the object’s further motion.     

Object motion tracking using a moving direction estimate and color updates

This paper presents a direction detection and tracking object color update algorithm used to track moving objects that change colors. Different from traditional color-based tracking methods, which use an initial color distribution in order to track objects as long as the object carries the full or partial initial color, this method introduces a color update method used to quickly find the new object color in a new location if the object changes its color partially or completely; the updated color is then used to locate the object. In our algorithm, an initial color pattern is used to track an object using the color. During the tracking, an object’s new location is at first estimated and then used to detect any color change. If the color has changed, a new color pattern is updated based on the changes in the previous color distribution, and then the new color pattern is used to calculate the current location of the object. This algorithm utilizes the property that the movement of an object can be estimated either by using the object’s shadow or by background subtraction. The implementation of our algorithm results in an effective real-time object tracking. The validity of the approach is illustrated by the presentation of experiment results obtained using the methods described in this paper.     

A color-based particle filter for multiple object tracking in an outdoor environment

Tracking multiple objects is more challenging than tracking a single object. Some problems arise in multiple-object tracking that do not exist in single-object tracking, such as object occlusion, the appearance of a new object and the disappearance of an existing object, updating the occluded object, etc. In this article, we present an approach to handling multiple-object tracking in the presence of occlusions, background clutter, and changing appearance. The occlusion is handled by considering the predicted trajectories of the objects based on a dynamic model and likelihood measures. We also propose target-model-update conditions, ensuring the proper tracking of multiple objects. The proposed method is implemented in a probabilistic framework such as a particle filter in conjunction with a color feature. The particle filter has proven very successful for nonlinear and non-Gaussian estimation problems. It approximates a posterior probability density of the state, such as the object’s position, by using samples or particles, where each state is denoted as the hypothetical state of the tracked object and its weight. The observation likelihood of the objects is modeled based on a color histogram. The sample weight is measured based on the Bhattacharya coefficient, which measures the similarity between each sample’s histogram and a specified target model. The algorithm can successfully track multiple objects in the presence of occlusion and noise. Experimental results show the effectiveness of our method in tracking multiple objects.     

Browsing the environment with the SNAP&TELL wearable computer system

This paper provides an overview of a multi-modal wearable computer system, SNAP&TELL. The system performs real-time gesture tracking, combined with audio-based control commands, in order to recognize objects in an environment, including outdoor landmarks. The system uses a single camera to capture images, which are then processed to perform color segmentation, fingertip shape analysis, robust tracking, and invariant object recognition, in order to quickly identify the objects encircled and SNAPped by the user’s pointing gesture. In addition, the system returns an audio narration, TELLing the user information concerning the object’s classification, historical facts, usage, etc. This system provides enabling technology for the design of intelligent assistants to support “Web-On-The-World” applications, with potential uses such as travel assistance, business advertisement, the design of smart living and working spaces, and pervasive wireless services and internet vehicles. An erratum to this article can be found at An erratum to this article can be found at     

The leader-follower formation control of nonholonomic mobile robots

In this paper, the leader-waypoint-follower formation is constructed based on relative motion states of nonholonomic mobile robots. Since the robots’ velocities are constrained, we proposed a geometrical waypoint in cone method so that the follower robots move to their desired waypoints effectively. In order to form and maintain the formation of multi-robots, we combine stable tracking control method with receding horizon (RH) tracking control method. The stable tracking control method aims to make the robot’s state errors stable and the RH tracking control method guarantees that the convergence of the state errors tends toward zero efficiently. Based on the methods mentioned above, the mobile robots formation can be maintained in any trajectory such as a straight line, a circle or a sinusoid. The simulation results based on the proposed approaches show each follower robot can move to its waypoint efficiently. To validate the proposed methods, we do the experiments with nonholonomic robots using only limited on-board sensor information.     

Privacy-enhanced Key Recovery in mobile communication environments

Generally, in mobile communication environments, Wireless Authentication and Key Establishment (WAKE) protocols are essential for secure information transmission. Key Recovery (KR) protocols, including WAKE protocols, can enable an authorized third party to gain access to encrypted message data under certain lawful circumstances. However, it is not easy to construct secure and strongly privacy-preserved KR protocols for mobile communication environments due to mobile hardware resources’ limitation. In this paper, we propose a security-enhanced KR protocol and then we construct a privacy-enhanced KR protocol, which is based on the security-enhanced KR protocol, for protecting users’ location privacy.     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

3D Reconstruction by Shadow Carving: Theory and Practical Evaluation

Cast shadows are an informative cue to the shape of objects. They are particularly valuable for discovering object’s concavities which are not available from other cues such as occluding boundaries. We propose a new method for recovering shape from shadows which we call shadow carving. Given a conservative estimate of the volume occupied by an object, it is possible to identify and carve away regions of this volume that are inconsistent with the observed pattern of shadows. We prove a theorem that guarantees that when these regions are carved away from the shape, the shape still remains conservative. Shadow carving overcomes limitations of previous studies on shape from shadows because it is robust with respect to errors in shadows detection and it allows the reconstruction of objects in the round, rather than just bas-reliefs. We propose a reconstruction system to recover shape from silhouettes and shadow carving. The silhouettes are used to reconstruct the initial conservative estimate of the object’s shape and shadow carving is used to carve out the concavities. We have simulated our reconstruction system with a commercial rendering package to explore the design parameters and assess the accuracy of the reconstruction. We have also implemented our reconstruction scheme in a table-top system and present the results of scanning of several objects.     

Dynamic Modeling and Tracking Control of a Nonholonomic Wheeled Mobile Manipulator with Dual Arms

This paper presents methodologies for dynamic modeling and trajectory tracking of a nonholonomic wheeled mobile manipulator (WMM) with dual arms. The complete dynamic model of such a manipulator is easily established using the Lagrange’s equation and MATHEMATICA. The structural properties of the overall system along with its subsystems are also well investigated and then exploited in further controller synthesis. The derived model is shown valid by reducing it to agree well with the mobile platform model. In order to solve the path tracking control problem of the wheeled mobile manipulator, a novel kinematic control scheme is proposed to deal with the nonholonomic constraints. With the backstepping technique and the filtered-error method, the nonlinear tracking control laws for the mobile manipulator system are constructed based on the Lyapunov stability theory. The proposed control scheme not only achieves simultaneous trajectory and velocity tracking, but also compensates for the dynamic interactions caused by the motions of the mobile platform and the two onboard manipulators. Simulation results are performed to illustrate the efficacy of the proposed control strategy.     

Incremental Learning for Robust Visual Tracking

Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.     

On-line modeling for real-time 3D target tracking

Model-based object tracking can provide autonomous mobile robotic systems with real-time 6-dof pose information, for example, enabling them to rendezvous with targets from a particular desired direction. Most existing model-based trackers, however, require the geometric model of the target to be known a priori, which may pose a practical problem in real-world environments. This paper presents a novel 3D modeler capable of building an approximate model of a target object on-line. The proposed technique rapidly constructs a 3D tessellated enveloping mesh and uses projective texture mapping to further model the target object’s surface features. Separation of the target object from background clutter is achieved via customizable interest filters. The resulting real-time object-tracking system was tested extensively via simulations and experiments.     

Consistent mesh partitioning and skeletonisation using the shape diameter function

Mesh partitioning and skeletonisation are fundamental for many computer graphics and animation techniques. Because of the close link between an object’s skeleton and its boundary, these two problems are in many cases complementary. Any partitioning of the object can assist in the creation of a skeleton and any segmentation of the skeleton can infer a partitioning of the object. In this paper, we consider these two problems on a wide variety of meshes, and strive to construct partitioning and skeletons which remain consistent across a family of objects, not a single one. Such families can consist of either a single object in multiple poses and resolutions, or multiple objects which have a general common shape. To achieve consistency, we base our algorithms on a volume-based shape-function called the shape-diameter-function (SDF), which remains largely oblivious to pose changes of the same object and maintains similar values in analogue parts of different objects. The SDF is a scalar function defined on the mesh surface; however, it expresses a measure of the diameter of the object’s volume in the neighborhood of each point on the surface. Using the SDF we are able to process and manipulate families of objects which contain similarities using a simple and consistent algorithm: consistently partitioning and creating skeletons among multiple meshes.     

Adaptive Robust Control of an Omnidirectional Mobile Platform for Autonomous Service Robots in Polar Coordinates

This paper presents an adaptive robust control method for trajectory tracking and path following of an omni-directional wheeled mobile platform with actuators’ uncertainties. The polar-space kinematic model of the platform with three independent driving omnidirectional wheels equally spaced at 120 from one another is briefly introduced, and the dynamic models of the three uncertain servomotors mounted on the driving wheels are also described. With the platform’s kinematic model and the motors’ dynamic model associated two unknown parameters, the adaptive robust controller is synthesized via the integral backstepping approach. Computer simulations and experimental results are conducted to show the effectiveness and merits of the proposed control method in comparison with a conventional PI feedback control method.     

Online Approach for Spatio-Temporal Trajectory Data Reduction for Portable Devices

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but effcient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.     

Vision Based Target Tracking and Collision Avoidance for Mobile Robots

A real-time object tracking and collision avoidance method is presented for mobile robot navigation in indoors environments using stereo vision and a laser sensor. Stereo vision is used to identify the target and to calculate its relative distance from the mobile robot while laser based range measurements are utilized to avoid collision with surrounding objects. The target is tracked by its predetermined or dynamically defined color. The mobile robot’s velocity is dynamically adjusted according to its distance from the target. Experimental results in indoor environments demonstrate the effectiveness of the method.     

MAHRU-M: A mobile humanoid robot platform based on a dual-network control system and coordinated task execution

This paper introduces a mobile humanoid robot platform able to execute various services for humans in their everyday environments. For service in more intelligent and varied environments, the control system of a robot must operate efficiently to ensure a coordinated robot system. We enhanced the efficiency of the control system by developing a dual-network control system. The network system consists of two communication protocols: high-speed IEEE 1394, and a highly stable Controller Area Network (CAN). A service framework is also introduced for the coordinated task execution by a humanoid robot. To execute given tasks, various sub-systems of the robot were coordinated effectively by this system. Performance assessments of the presented framework and the proposed control system are experimentally conducted. MAHRU-M, as a platform for a mobile humanoid robot, recognizes the designated object. The object’s pose is calculated by performing model-based object tracking using a particle filter with back projection-based sampling. A unique approach is used to solve the human-like arm inverse kinematics, allowing the control system to generate smooth trajectories for each joint of the humanoid robot. A mean-shift algorithm using bilateral filtering is also used for real-time and robust object tracking. The results of the experiment show that a robot can execute its services efficiently in human workspaces such as an office or a home.     

Modeling and synthesis of supervisory control based on Petri nets for distributed objects. I. Interaction mechanism and the basic method

We describe a methodology of discrete event modeling for a class of distributed objects and their required behavior (specifications) for the design of real time automation systems. In our methodology, we use the structured discrete event system (SDES2) model: on the first stage, it is used to analyze the functionality and coherence of the object and its specification; on the second, we propose for SDES2 a basic synthesis method that works for the models of object and supervisor based on Petri nets (both modeling and controlling). At the same time, we propose to synthesize the supervisor as a Petri net embedded in SDES2 with a feedback circuit in order to restrict the object’s operation according to specification requirements. We propose an interaction mechanism for the modeling and controlling Petri nets with the object and the external environment. In essence, the interaction mechanism is an object control scheme based on the constructed net. This mechanism analyzes the current state of the object and computes the controls that should be passed on to the object’s actuators. Computations are done with a cyclic procedure looping over the matrix representation of the net.     

BuddyBeads: techno-jewelry for non-verbal communication within teenager girls groups

The extremity in teenagers’ attitudes and actions coupled with the opportunities of mobile communication creates new behaviors and re-shapes existing ones. But, however meaningful the phone is in teenagers’ lives, it is not designed to support their need for emotional communication and group identity. The BuddyBeads project suggests alternative communication forms among teenagers, which emphasize their social structures, behaviors and needs. BuddyBeads are techno-jewelry items that facilitate non-verbal and emotional communication among group members, through codes and signals which the group decided upon together. Each group member has a matching jewelry piece and can use it to communicate her emotional state to the other group members.