In single-incision laparoscopic surgery (SILS), magnetic anchoring and guidance system (MAGS) is a promising technique to prevent clutter in the surgical workspace and provide a larger vision field. Existing camera designs mainly rely on rigid structure design, resulting in risks of losing magnetic coupling and impacting tissue during the insertion and coupling procedure. In this paper, we proposed a wireless MAGS consisting of soft material and structure design. The camera can bend at the exit of the trocar and maintain strong coupling with the external actuator. The operation principle and modeling were established to investigate the parameter design. An easier insertion procedure was introduced and demonstrated in the experiment. The bendability was tested showing the camera could reach 20° in bending angle and 16.4mm in displacement. The insertion and deployment took less than 2 minutes on average.

Fully insertable laparoscopic cameras represent a promising future of minimally invasive surgery. The most characteristic technology adopted on these devices is transabdominal anchoring and actuation based on magnetic coupling. However, few have paid adequate attention to the safety concerns. As the camera is anchored against the interior abdominal wall without any force feedback, the patient is being exposed to a high risk of getting injured by inappropriate stress on the tissue. We have recovered the camera-tissue interaction force via a non-invasive approach in our previous work. Aiming to access the stress distribution, this paper presents a viscoelastic camera-tissue interaction model, which establishes explicit relations between the contact force and the stress distribution on the tissue. For the first time, a geometric constraint between the contact angle and the tissue indentation is introduced, which helps make the multivariable model solvable. Ex-vivo experiments on porcine abdomen tissue facilitated by non-invasive force measurement validate effectiveness of the model. This work lays foundation for improving control and surgical safety of using a magnetic actuated insertable laparoscopic surgical camera.

To improve the efficiency of medical visualization for computer aided surgery, we propose a fast and unsupervised 3D-CNN based non-local feature learning network. The proposed network consists of an encoder structure and a decoder structure. The encoder of the network projects the cube into a high-dimensional feature space, and the decoder of the network reconstructs the cube from the feature space. The decoder of the network serves as a dictionary shared by the cube to enforce the features for similar parts to be similar although they may distribute at disjointed locations. With such structures, the network is able to extract non-local features of the entire data. Moreover, a sparse constraint is incorporated into the network to increase the discriminative of the non-local features. Then the extracted non-local features of each voxel are fused with the corresponding position matrix and Hessian matrix for the voxel classification using Random Forest. Finally, a multidimensional transfer function is designed to enable the volume rendering. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with much less training time.

Understanding of characteristics of objects such as fragility, rigidity, texture and dimensions facilitates and innovates robotic grasping. In this paper, we propose a context- aware anthropomorphic robotic hand (MagicHand) grasping system which is able to gather various information about its target object and generate grasping strategies based on the perceived information. In this work, NIR spectra of target objects are perceived to recognize materials on a molecular level and RGB-D images are collected to estimate dimensions of the objects. We selected six most used grasping poses and our system is able to decide the most suitable grasp strategies based on the characteristics of an object. Through multiple experiments, the performance of the MagicHand system is demonstrated.

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95.51% for robotic grasping detection.

Fully insertable laparoscopic cameras feature more locomotive flexibility in a larger workspace compared to conventional trocar-based laparoscopes and thus represent a promising future of minimally invasive surgery. These cameras are principally anchored and actuated by transabdominal magnetic coupling. Although several proof-of-concept prototypes have shown the technical feasibility in terms of camera actuation and laparoscopic imaging, none of them are getting close to clinical practice due to concerns about safety. One common problem lies in that the interaction force between the camera and the abdominal wall tissue is completely unknown and not controlled. The camera is being manipulated in an open loop which exposes the patient to a high risk of being injured. In this paper, a noninvasive real-time camera-tissue interaction force measurement approach for an insertable laparoscopic camera is proposed, implemented, and validated.Ex-vivo experiments using a simulated abdominal cavity have demonstrated the effectiveness of this approach during anchoring, translation, and rotation camera behaviors. Potential surgical impacts enabled by the force feedback have also been exemplified by a robotic-assisted camera control experiment using shared autonomy.

This paper proposes a generic cleaning module to address lens fogging and soiling problems for insertable robotic cameras in laparoscopic surgery. The proposed lens cleaning module features minimal intraoperative interruption for surgeons to maintain clear visual field. The technical challenges for developing such a compact modular design involve confining the wiping mechanism within small space, yet delivering sufficient energy for the lens cleaning task. Inspired by thermo-activated phase transformation of shape memory alloy, we develop an effective mesoscale actuation mechanism to overcome the design challenges. A prototype is designed and manufactured for performance evaluation. The design effectiveness was verified by experiments.

Inverse dynamics modeling is a critical problem for the computed-torque control of robotic manipulator. This paper presents a novel recurrent network based on the modified Simple Recurrent Unit (SRU) with hierarchical memory (SRU-HM), which is achieved by the nested SRU structure. In this way, it enables the capability to retain the long-term information in the distant past, compared with the conventional stacked structure. The hidden state of SRU is able to provide more complete information relevant to current prediction. Experimental results demonstrate that the proposed method can improve the accuracy of dynamics model greatly, and outperforms the state-of-the-art methods.

The light-weight thermal-RGB-inertial sensing units are now gaining increasing research attention, due to their heterogeneous and complementary properties. A robust and accurate registration between a thermal-RGB camera and an inertial sensor is a necessity for effective thermal-RGB-inertial fusion, which is an indispensable procedure for reliable tracking and mapping tasks. This paper presents an accurate calibration method to geometrically correlate the spatial relationships between an RGB camera, a thermal camera and an inertial measurement unit (IMU). The calibration proceeds within the unified calibration framework (thermal-to-RGB, RGB-to-IMU). The extrinsic parameters are estimated by jointly optimizing both the chessboard corner reprojection errors and acceleration and angular velocity error terms. Extensive evaluations have been performed on the collected thermal-RGB-inertial measurements. In this experiments study, the average RMS translation and Euler angle errors are less than 6 mm and 0.04 rad respectively under 20% artificial noise.

This paper proposes a novel in-vivo robotic laparo-scopic camera design with an optimized illumination system, which is a crucial component for achieving high imaging quality. The robotic camera design with three extendable wings can reserve sufficient on-board space to harbor the optimized illumination system without affecting the compactness of the camera. We contribute a freeform optical lens design method and develop three miniature optical lenses for the LEDs to achieve greater than 95% illumination uniformity, greater than 14, 000 lx illuminance on a target plane with a distance of 100 mm, and greater than 89% optical efficiency. The prototype is implemented and experimentally tested, which demonstrates great performance of the in-vivo robotic laparoscopic camera and the significance of the optimized illumination system.

To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way. To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data. On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements. Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

Robotic camera systems have recently drawn attention in minimally invasive surgeries(MIS). Control and manipulation of these systems during traversing abdominal cavity is associated with camera-tissue interaction. This paper demonstrates a theoretical and experimental analysis of a wireless laparoscopic camera's interaction with abdominal wall during MIS. A mechanical model is developed to represent the behavior of abdominal wall bulk tissue by considering skin, fat, muscle and connective tissues layers which predicts behavior of the tissue during interaction with laparoscopic camera. The model was implemented in ABAQUS to analyze the camera-tissue interaction and find interaction forces generated during contact and motion with different linear and rotational speeds. Simulations were validated by experiments on porcine tissue which can be used for proper control of insertable camera. A noninvasive method is proposed to measure the mechanical properties of each patient's abdominal wall tissue at start of MIS in order to tune the control system to optimize the interaction depth and opposite forces. This method features preventing overload damage and camera fall during MIS.

To improve the search efficiency of robotic grasping detection, this paper presents a novel search algorithm based on the image pyramid. It significantly reduces the search space for grasping position detection using the coarse-to-fine strategy. The proposed method searches the positions from the top layer of the pyramid, and initializes the search area at the next layer. The sparse automatic encoder is employed to construct the model which is used to evaluate the grasp quality. The experimental results demonstrate that the proposed search algorithm can improve efficiency of the robotic grasping detection with the comparative performance on the grasp quality.

Robotic systems have recently drawn attention in minimally invasive surgeries due to their increased dexterity feature. A major drawback of these systems is image blurring due to lens contamination which cause imaging impairment during up to 40% of surgery time. This paper demonstrates a novel laparoscopic magnetic driven camera system with implemented in-vivo lens cleaning and debris prevention systems. This camera robot can cover 150 degrees field of view inside the abdominal cavity and provide adjustable illumination system to improve the video quality. Design details for different modules, such as anchoring, actuation, video capturing, illumination, debris prevention and lens cleaning of this robot have been provided and discussed. This camera robot can decrease the possibility of lens contamination by creating CO2gas barrier in front of lens. In case of contamination it can clean the lens in-vivo without removing the camera from abdominal cavity. The lens cleaning module has been tested for water vapor and water droplets. The robot is manufactured and each module has been validated by designed experiments.

Combining light-weight visual and inertial modalities for motion capturing has been popular in robotics researches. There exist scale ambiguity, inaccurate pose estimation with little or no baseline, incremental drifts over time in visual-inertial fusion. Thus, in this paper, we propose a robust motion capturing method based on the multi-joint kinematic chain using monocular visual-inertial sensors. Our method is able to recover monocular visual scale through the joint geometry constraint. Additionally, we take inertial pre-integration to assist visual outlier removal using Maximum A Posteriori method. Ultimately, the kinematic chain model is leveraged to constrain the associated multiple visual-inertial estimation drifts during long time tracking. In the experiments, we conduct multi-joint capturing on a robotic arm. The quality of motion reconstruction is evaluated by comparing the estimated results with the measurements from an optical motion tracking system OptiTrack.

This paper presents an orientation estimation methods using inertial cues (IMU) and visual feature constraint. Our proposed approach combines both of these two modalities in an original way. Two feature-point correspondences between consecutive frames are firstly selected that not merely meet the requirement of descriptor similarity constraint but the locality constraint. Secondly, these two selected correspondences together with inertial quaternions are jointly employed to derive the initial body pose. Thirdly, a coarse-to-fine procedure proceeds in removing visual false matches and in estimating body poses iteratively using the Posteriori Bayes Rule and Expectation Maximization. Eventually, the optimal orientation is estimated via the iteratively selected visual inliers. Experimental results validate that our proposed strategy is effective and accurate in orientation estimate.

Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.

This paper presents the design of a novel insertable robotic capsule camera system for single incision laparoscopic surgery. This design features a unified mechanism for anchoring, navigating, and rotating an insertable camera by externally generated rotational magnetic field. The design is inspired by the spherical motor concept where the external stator generates anchoring and rotational magnetic field to control the motion of the insertable robotic capsule camera. The insertable camera body, which has no active locomotion mechanism onboard, is capsulated in a one-piece housing with two ringshaped tail-end magnets and one cylindrical central magnet embedded on-board as a rotor. The stator positioned outside an abdominal cavity consists of both permanent magnets and electromagnetic coils for generating reliable rotational magnetic field. The initial prototype results in a compact insertable camera robot with a 12.7mm diameter and a 68mm length. The design concepts are analyzed theoretically and verified experimentally. The experiments validate that the proposed capsule robot design provides reliable camera fixation and locomotion capabilities under various testing conditions.

This paper proposes an ego-motion tracking method that utilizes visual-inertial sensors for wearable blind navigation. The unique challenge of wearable motion tracking is to cope with arbitrary body motion and complex environmental dynamics. We introduce a visual sanity check to select accurate visual estimations by comparing visually estimated rotation with measured rotation by a gyroscope. The movement trajectory is recovered through adaptive fusion of visual estimations and inertial measurements, where the visual estimation outputs motion transformation between consecutive image captures, and inertial sensors measure translational acceleration and angular velocities. The frame rates of visual and inertial sensors are different, and vary with respect to time owning to visual sanity checks. We hence employ a multirate extended Kalman filter (EKF) to fuse visual and inertial estimations. The proposed method was tested in different indoor environments, and the results show its effectiveness and accuracy in ego-motion tracking.

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.

Wearable inertial tracking is well accepted due to its convenience for free-style motion tracking with high accuracy. Traditionally, complicated high-order calculations for human kinematic modeling and inaccurate estimation of sensor placement are interfering the efficiency of real-time tracking. In order to tackle the challenges, a wearable human motion tracking system is developed by applying twists and exponential maps techniques. When the body segments are articulated by product of exponential maps, joint positions are continuously updated based on these techniques and their rotational angles are represented individually within the global frame. It is more efficient to achieve real-time motion tracking with low-order calculations. Meanwhile by applying the well-designed calibration procedure, it is more convenient to estimate a sensor's position and orientation regardless of knowing its placement. This paper presents our approach and exemplifies the assessment of proposed motion tracking system by several tests of limb and full body motion tracking. The comparisons with Vicon and OptiTrack motion capture systems verify satisfactorily high accuracy.

This paper proposes a method to estimate the motion of ambient objects including translational and rotational velocities by moving observers with hybrid visual-inertial sensors. Ambient motion is recovered from visual optical flows that represent ego and ambient dynamics. In this paper, each moving object is considered as a rigid body that has been segmented from the background using computer vision algorithms. In motion recovery, the fundamental challenge is to resolve the coupling between scene depths and translational velocities. Ambient rotational velocities are obtained following a depth-independent bilinear constrain. The scales of ambient trans-lational velocities is computed using the proposed dynamics constraint with an assumption that ambient accelerations are negligible. A fix-point optimization scheme is further introduced to iteratively refine the recoveries of translational and rotational ambient motion until an expected precision is achieved or the maximal iteration is reached. During the optimization, translational ambient motion is precisely recovered and translational ambient motion is rescaled to the canonical amplitude. The results of the simulation study show the effectiveness of the proposed method in motion analysis and prediction.

Motion planning is an important problem with many applications in robotics. In this paper, we focus on motion planning with rectangular obstacles parallel to the X, Y or Z axis. We formulate motion planning using Satisfiability Modulo Theories (SMT) and use SMT solvers to find a feasible path from the source to the goal. Our formulation decompose the robotic path into N path segments where the two ends of each path segment can be constrained using difference logic. Our SMT approach will find a solution if and only if a feasible path exists for the given constraints. We present extensive experimental results to demonstrate the scalability of our approach.

This paper proposes a unified active locomotion mechanism for a capsule-shaped laparoscopic surgical camera system. The proposed design integrates the camera's fixation and manipulation together by adjusting a 3D rotational magnetic field from a stator outside a patient's body. The stator generates both torque to rotate the inside rotor dome in all three dimensions, and force to serve as an anchoring system that keeps the camera steady during a surgical procedure. This design eliminates the need for an articulated design and therefore the integrated motors to significantly reduce the size of the camera. A set of stator and rotor designs are developed and evaluated by simulations and experiments.

The trajectories of mobile seeds have a great influence on localization accuracy and efficiency. This paper presents a novel information-driven intelligent mobility-assisted wireless sensor network localization algorithm. Without requiring any prior knowledge of the sensing field, seeds' or pseudo-seeds' (common sensors which have been positioned) trajectories are scheduled dynamically aiming at position estimates of neighboring non-positioned common sensors. With an information-theoretic utility measure as the objective function, mobile seeds or pseudo-seeds actively determine their motion directions for minimizing the uncertainty in position estimates of neighboring sensors. At the first level, seeds estimate the neighboring sensor nodes' positions with bearing measurements by means of extended Kalman filters and optimize their motion directions by maximizing the mutual information between the position estimates and the motions of seeds. Afterwards the seeds forward the position estimates to the corresponding sensor nodes, which then act as pseudo-seeds. By repeating this process at the following levels, all sensor nodes can obtain position estimates. Compared with heuristic mobility and random mobility-assisted mobile sensor network localization algorithms, the proposed algorithm requires fewer maneuvers of seed or pseudo-seeds for quick convergence to good position estimates. Extensive simulations show that this algorithm can provide more accurate position estimates with fewer maneuvers, especially in the case of limited seeds.

Direct volume rendering (DVR) is commonly employed for the medical visualization. Multi-dimensional transfer functions are used in DVR to emphasize the region of interest in details. However, it is impractical to interact directly with the functions in more than three dimension. This paper proposes a novel framework called geometry constrained sparse embedding (GCSE) for dimensionality reduction (DR). GCSE allows the conventional DR methods to be applied to a dictionary with much smaller atoms instead. The mapping derived from the dictionary feeds to the original features to obtain the ones in the reduced dimension. To obtain a good dictionary, the intrinsic structure of features is encoded in the sparse embedding based on a geometry distance. In addition, stochastic gradient descent algorithm is employed to speed up the dictionary learning. Various experiments have been conducted using both synthetic and real CT data sets. Compared with conventional methods, GCSE not only produces the comparable results, but also performs well with the capability to handle the large data set more powerfully. The rendering results using the real CT data has demonstrated the effectiveness of GCSE.

Recently, the open-source robot operating system (ROS) has been growing rapidly in the robotics community. However, the ROS runs on Linux, which does not provide timing guarantees for robot motion. This paper present a hybrid real-time ROS architecture on multi-core processor “RGMP-ROS”, which consists of two parts including the non-real-time subsystem “GPOS (General Operating system)” and the real-time one “RTOS (Real-time Operating system)”. The GPOS is comprised of non-real-time ROS nodes running in Linux, while the RTOS only contains real-time ROS nodes running in Nuttx. To get higher operational efficiency, the RGMP-ROS system is executed by a dual-core processor, one CPU for GPOS and the other for RTOS. The RGMP-ROS has used in the controller of a 6-DOF modular manipulator, and its effectiveness and efficiency are demonstrated by software testing and experiments. The main contributions of the present work lie in the realization of real-time ROS architecture and the application of multi-core processor in the hybrid control of an industrial robot.

This paper proposes an active locomotion mechanism for a wireless laparoscopic surgical camera. The mechanism consists of a stator with 17 iron-core coils and a rotor with 3 cylindrical permanent magnets inside the camera. Our motor-free design unifies the camera's fixation and manipulation by adjusting input currents in the stator which generates 3D rotational magnetic fields, and decouples the camera's locomotion into pan motion and tilt motion. In the simulation studies, our proposed design can conservatively achieve 360° pan motion with a 22.5° resolution, and 127° ~ 164° maximum tilting range for tilt motion which depends on tilt motion working modes and the distance between the rotor and the stator.

Robust navigation for mobile robots requires an accurate method for tracking the robot position in the environment. This paper presents a simple and novel visual-inertial integration system suitable for unstructured and unprepared indoor environments, where MARG (Magnet, Angular Rate and Gravity) sensors and a monocular camera are used. The pre-estimated orientation from MARG sensors, is used to estimate the translation based on the data from the visual and inertial sensors. This has a significant effect on the performance of the fusion sensing strategy and makes the fusion procedure much easier, because the gravitational acceleration can be correctly removed from the accelerometer measurements before the fusion procedure, where a linear Kalman Filter is selected as the fusion estimator. the use of pre-estimated orientation can help to eliminate erroneous point matches based on the properties of the pure camera translation and thus the computational requirements can be significantly reduced compared to the RANSAC (RANdom SAmple Consensus) algorithm. In addition, an adaptive-frame-rate single camera is selected to not only avoid motion blur based on the angular velocity and acceleration after compensation but also to make an effect called visual zero-velocity update for the static motion. Thus, it can recover a more accurate baseline and meanwhile reduce the computational requirements. In particular, an absolute scale factor, which is usually lost in monocular camera tracking, can be obtained by introducing it into the estimator. Simulation and experimental results are presented for different environments with different types of movement and the results from a Pioneer robot are used to demonstrate the accuracy of the proposed method.

This paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered.

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. A design of a real-time wearable ECG monitoring system with cardiac arrhythmia classification is proposed in this paper. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Last, human activities by an accelerometer can be identified to reduce the chance of false alarm in classification due to the motion artifacts.

The purpose of this paper is to develop an automatic camera phone based multi-view food classifier as part of a food intake assessment system. Food intake assessment is important for obesity management, which has shown significant impacts in public healthcare. Conventional dietary record based food intake assessment methods exhibit insufficient popularity due to their low accuracy and high dependence on human interactions. Image based food recognition appears recently. But it is still under development and far away from field applications. This paper presents DietCam, a camera phone based application to evaluate food intakes automatically from multiple perspectives. Food recognition from images is afflicted currently with a low recognition accuracy caused by the uncertainties of food appearances. The deformable nature of food items together with the complex background environment makes the problem even harder. DietCam separates every food item through evaluating the best perspective and recognize each of them from multiple images with a probabilistic method. The recognition accuracy is increased through an enhanced joint distribution from every viewpoint. A prototype of DietCam has been implemented on iPhone. In the field experiments, it shows an accuracy of 84% for regular shape food items.

This paper presents a wearable Inertial Measurement Unit pedestrian positioning system for indoors. Hidden Markov Model (HMM) is introduced to pre-process the sensor data and classify common activities. HMM also complements local minimum angular rate value for capturing the onset/end of each step. ZUPT algorithm are implemented to correct the walking velocity at step stance phase when errors existed. A novel acceleration-based approach combined with gyroscope data is developed to achieve a better heading estimation. Proposed method is able to reduce drift errors from gyroscopes and avoid electromagnetic perturbance to magnetometers when estimate subject's position. Experiment results show the positioning system achieves approximately 99% accuracy.

This paper presents an adaptive sparse sampling approach based on mobile robotic sensors. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. In this paper a feedback driven algorithm is discussed, where new measurements are determined based on the analysis of existing observations under a sparse domain. More specifically, Wavelet structure is considered to optimize measurement projections to substantially reduce the number of measurements based on compressive sensing framework. Sensor motion is designed based on the distribution of optimal measurements, striking a balance between moving cost and measurement value. Simulation results are presented to compare the performance with normal compressive sensing method that uses random measurements and other adaptive sampling methods.

Self-assembly is a process during which pre-existing components are autonomously organized into some special patterns or structures without human intervention. In this paper, we propose a new control algorithm on distributed self-assembly which is implemented on the Sambot robot platform. A directional self-assembly control model is proposed, in which a configuration connection state table is used to represent the configuration of the robotic structures composed of multiple Sambots. There are three types of Sambots, docking Sambots, SEED Sambot and Connected Sambots. All docking Sambots adopt behavior-based controller that is independent of target configuration. The SEED Sambot and Connected Sambots are used to implement configuration growth. Self-assembly experiments of snake-like and quadruped configurations are conducted on the Sambot platform with five Sambots. The experimental results show the effectiveness and scalability of the distributed self-assembly algorithm.

In this paper, a novel time division multiple access based MAC protocol designed for body sensor networks (BSNs) is presented. H-medium-access control (MAC) aims to improve BSNs energy efficiency by exploiting heartbeat rhythm information, instead of using periodic synchronization beacons, to perform time synchronization. Heartbeat rhythm is inherent in every human body and observable in various biosignals. Biosensors in a BSN can extract the heartbeat rhythm from their own sensory data by detecting waveform peaks. All rhythms represented by peak sequences are naturally synchronized since they are driven by the same source, i.e., the heartbeat. Following the rhythm, biosensors can achieve time synchronization without having to turn on their radio to receive periodic timing information from a central controller, so that energy cost for time synchronization can be completely eliminated and the lifetime of the network can be prolonged. An active synchronization recovery scheme is also developed, including two resynchronization approaches. The algorithms are simulated using the discrete event simulator OMNet + + with real-world data from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital multiparameter database Multiparameter Intelligent Monitoring for Intensive Care. The results show that H-MAC can prolong the network life dramatically.

This paper presents an adaptive and decentralized robotic cooperation algorithm for controlling the mobile sensors to form a chained network and maintaining the communication links. A single-layer and double-layer chain tethering algorithms are developed for exploring the open and constrained environments by mobile robots. A comprehensive metric for finding the optimal communication range is introduced. With the measurements, mobile robots could be organized into an optimal chained form for tethering. The tethering algorithm could detect the failed nodes and reconfigure the system. It offers an adaptive solution to broken communication links.

This paper presents a novel approach, compressive mobile sensing, to use mobile sensors to sample and reconstruct sensing fields based on compressive sensing. Compressive sensing is an emerging research field based on the fact that a small number of linear measurements can recover a sparse signal without losing any useful information. Using compressive sensing, the signal can be recovered by a sampling rate that is much lower than the requirements from the well-known Shannon sampling theory. The proposed compressive mobile sensing approach has not only the merits of compressive sensing, but also the flexibility of different sampling densities for areas of different interests. A special measurement process makes it different from normal compressive sensing. Adopting importance sampling, compressive mobile sensing enables mobile sensors to move adaptively and acquire more samples from more important areas. A motion planning algorithm is designed based on the result of sparsity analysis to locate areas of more interests. At last, experimental results of 2-D mapping are presented as an implementation compressive mobile sensing.

Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes.

Dynamics modeling and analysis of a tiny swimming robot, which is composed of a helix type head and an elastic tail, is presented in this paper. The microrobot is designed for controlled drug delivery. It is at the micrometer scale and suitable for a swimming environment under low Reynolds number (Re). The head of the swimming robot is driven by an external rotating magnetic field, which enables it to be operated wirelessly. The spiral-type head accommodates communication and control units and serves as the base for the elastic tail. When a rotating magnetic field is applied, the head rotates synchronously with the field, generating and propagating driving torque to the straight elastic tail. When the driving torque reaches a threshold, dramatic deformation takes place on the elastic tail. The tail then transforms into a helix and generates propulsive thrust. The entire tail also serves as a drug reservoir. This paper focuses on analyzing the dynamics of the microrobot using resistive force theory (RFT), and comparing the propulsion performance with other rigid-body microrobots.

A novel approach for segmenting ECG signal in a body sensor network employing hidden Markov modeling (HMM) technique is presented. The parameter adaptation in traditional HMM methods is conservative and slow to respond to these beat interval changes. Inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate. To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm. Body sensor networks are used to pre-segment the raw ECG data by performing QRS detection. Instead of one single generic HMM, multiple individualized HMMs are used. Each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features from the same group, so that the temporal parameter adaptation can be naturally achieved.

H-MAC, a novel time division multiple access (TDMA) MAC protocol, aims to improve body sensor networks (BSNs) energy efficiency by exploiting heartbeat rhythm information to perform time synchronization. Heartbeat rhythm is inherent in every human body and observable in various biosignals. Biosensors in a BSN can extract the heartbeat rhythm from their own sensory data by detecting waveform peaks. Following the rhythm, biosensors can achieve time synchronization without having to turn on their radio to receive periodic timing information from a central controller, so that energy cost for time synchronization can be completely eliminated and the lifetime of network can be prolonged. An active synchronization recovery scheme is also developed, in which two resynchronization approaches can be triggered by detected abrupt peak interval changes. The algorithms are verified using real world data from MIT-BIH multi-parameter database MIMIC.

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust.

A sensor network is generally composed of a set of sensors with limited computation capability and power supply. Thus, a well-defined resource allocation scheme is essential for maintaining the whole sensor network. This paper investigates the dynamic resource allocation problem in a sensor and robot network for mobile target tracking tasks. Most of the sensors will be in sleep mode except for the ones that can contribute for tracking. The sensor network resource allocation is achieved by a hierarchical structure-clustering. Upon detecting an interesting event, a set of sensors form a cluster. Only cluster members will be activated during the tracking task. The cluster headship and membership will be updated based on the target’s movement properties. In this paper, the clustering algorithm considers sensing area with communication holes and a routing tree is set up within the cluster. For a cluster with communication and/or sensing holes, mobile sensors will be deployed to enhance the sensing and communication capability in the clustering area. Simulations have been used to verify the proposed algorithm.

This paper discusses how a biped robot with heterogeneous legs imitates a person's walking from gait design, gait planning and gait control. A biped robot with heterogeneous legs (BRHL) robot consists of an artificial leg and an intelligent bionic leg. The purpose of this robot's design is to make the intelligent bionic leg follow the artificial leg's movement, which provides an excellent platform for the research of intelligent prosthetic leg. After the introduction of gait design and gait planning, a semi-active kneel control method is proposed for magnetorheological (MR) damper in the intelligent bionic leg. And an overall control system scheme is presented. Simulative and practical system experiments prove the validity of the presented plan and proposed algorithm

With the development of research on wireless sensor networks (WSNs), localization in the WSNs has become a very important research point. At present, there are mainly two kinds of approaches, range-based approach and range-free approach. Localization precision of range-based approach is higher than the range-free approach. In the range-based approach, time difference of arrival (TDOA) method needs less requirements for time synchronization. This paper proposes a high precision localization algorithm based on TDOA, which utilizes average value of time difference by rolling average to decrease the measurement error, and adopts unconstrained least squares (LS) estimator to achieve the accurate localization. Simulation results and error analysis prove its validity

The design of a tiny swimming robot, which is composed of a spiral-type head and an elastic tail, is proposed in this paper. The microrobot is designed for controlled drug delivery as well as a wide range of biomedical applications. It is at the millimeter scale and suitable for swimming under low Reynolds number (Re) environment. The head part of the swimming robot is driven by external rotating magnetic fields, which enables it to be operated wirelessly. The spiral-type head accommodates communication and control units and serves as the base for the elastic tail. When a rotating magnetic field is applied, the head rotates synchronously with the field, generating and propagating driving torque to the straight elastic tail. When the driving torque reaches a threshold, dramatic deformation takes place on the elastic tail. The tail then transforms into a helix and generates helpful propulsive thrust. The entire tail also serves as a drug reservoir in controlled drug delivery operations. This paper's focus is to analyze the dynamics of the microrobot using resistive force theory (RFT), and compare the propulsion performance with other rigid-body microrobots

Blood vessel cleaning and enlargement are important medical practices in medicine. Traditional hard-touch methods, such as cardiac catheterization, have high risk of damaging blood vessels due to the mechanical friction mechanism. In this paper, a micro-particle based shooting and harvesting system for blood vessel cleaning and enlargement is proposed. This method avoids the hard-touch and reduces the risk of damaging blood vessels. A mathematical model to describe the dynamics of the system is presented. The goal is to better understand the cleaning and enlargement dynamics; so that effective operating strategies can be applied. Simulation results are presented to show the effectiveness of the dynamics model

This paper presents a multi-robot coordination architecture for a robot-sensor network to track and intercept targets. For a target tracking and interception task, the sensor network continuously tracks the targets and dynamically selects robots to intercept the target. The robots are navigated through the sensor network. The main contribution of this paper lies on providing a scalable, power saving robot selection algorithm for the sensor networks. The robot selection algorithm is addressed based on partitioning among sensor nodes. Through partitioning, the sensor nodes are grouped so that they know which robot to choose if it is the closest to the target. The partitioning is updated with respect to the movement of robots. The proposed algorithms are proven to be effective and verified by simulations. Some analytic investigation on the communication overhead in the sensor networks is also provided

Sensor network, which has integrated wireless communication, date collection and information processing capacities, is a booming technique in information collecting. With the development of research on wireless sensor networks (WSNs), localization in the WSNs has become a very important research point. At present, there are mainly two kinds of approaches, range-based approach and range-free approach. Localization precision of range-based approach is higher than the range-free approach. In the range-based approach, time difference of arrival (TDOA) method needs less requirements for time synchronization. This paper proposes a self-localization approach based on TDOA, which utilizes average value of time difference by rolling average to decrease the measurement error, and adopts unconstrained least squares (LS) estimator to achieve the accurate localization. Simulation results and error analysis prove its validity

This paper presents gait perception and coordinated control of a Biped robot with heterogeneous legs (BRHL), which is a new type of humanoid robot. This paper first introduces the concepts and research objectives of a BRHL. Then configuration of a BRHL is discussed in detail. The coordinated dynamics model and MR damper model are given. Rules based on gait perception are introduced. In the end, this paper discusses the control structure of a BRHL. Simulation and prototype of a BRHL are discussed. The research indicates that intelligent bionic leg controlled by MR damper can track artificial leg's gait well. A BRHL provides an ideal test-bed for advanced intelligent prosthesis

For the aircraft structure inspection, this paper introduces a micro biped robot with inspection probe and wireless vision, analyzes the robot locomotion modes and dynamic models, and studies the motion control algorithm. Considering the movement flexibility caused by five degrees of freedom, a hierarchy structure is presented for the robot motion control system. For the long distance locating problem, a relay locating approach is presented to solve robot locating and to estimate the orientation of the robot by using vision and distance information from encoder and CAD model. The movement orientation can be adjusted rivet by rivet in the inspection process. The experimental results show that the control algorithm works well, and orientation estimation algorithm provides an acceptable orientation precision for continuous rivet inspection.

Sensor networks are attracting attention in several fields. However, the feasibility of such networks faces several challenges, two of which are data fusion and error reduction. This paper presents data fusion and high level error correction algorithms for sensor networks. These algorithms are scalable and general, and thus can be applied to networks of any size using any type of sensors. The data fusion procedure developed results in significant reduction of data sent without reducing the amount of information provided. This allows for real-time remote monitoring of information across low bandwidth connections such as the Internet. The high level error reduction is accomplished using a probability matrix and results in a significant amount of error elimination. A sensor network capable of tracking object motion is constructed to evaluate the performance of the two algorithms. The experimental results obtained confirmed the theory presented.

This paper presents a mobile sensor deployment algorithm within a hybrid sensor network, which consists of some mobile sensors and a relatively large number of static sensors. For a target tracking task, a cluster is constructed and updated dynamically for tracking upon detection of an event. A cluster construction algorithm in a sparsely deployed sensor network is addressed. Mobile sensors are used to fill the coverage holes inside the cluster. A scalable mobile sensor deployment algorithm based on static sensor cluster is presented and analyzed. Minimum spanning circle (MSC) of a polygon acts as the metric for dynamic clustering. Simulations have been used to verify the proposed algorithms.

This paper discusses a range free localization algorithm for the wireless sensors in a hybrid sensor network, which consists of a large number of static sensors and relatively small number of mobile robots. The mobile robots are equipped with either global positioning systems (GPS) or other localization devices. By moving the mobile robots and broadcasting their locations in the sensor network, the static sensors are able to estimate their position based on the messages received. A mobile robot navigation in a un-localized sensor network is further developed. As and when an event occurs in the sensor network, a mobile robot can be navigated by the un-localized sensor nodes to the event location, which in turn are finely localized by the mobile robot. The mobile robot, equipped with advanced sensing and communication capabilities, can enhance the sensing in the area of event location. This algorithm works well in an event driven network wherein both the localization of the sensor nodes and navigation of the mobile robot to the event is done simultaneously. As the frequency of the event increases the nodes are better localized and the precision of the node's location becomes finer. Simulation results have been used to verify the effectiveness of the proposed algorithms.

Hybrid sensor networks comprise of mobile and static sensor nodes setup for the purpose of collaboratively performing tasks like sensing a phenomenon or monitoring a region. In this paper, we present a novel approach for navigating a mobile sensor node (MSN) through such a hybrid sensor network. The static sensor nodes in the sensor network guide the MSN to the phenomenon. One or more MSN’s are selected based on their proximity to the detected phenomenon. Navigation is accomplished using the concepts of credit based field setup and navigation force from static sensor nodes. Our approach does not require any prior maps of the environment thus, cutting down the cost of the overall system. The simulation results have verified the effectiveness of the proposed approach. In each of the simulation runs, the static sensor nodes were able to successfully guide the MSN towards the phenomenon.

Spray forming is an emerging manufacturing process. The automated tool planning for this process is a nontrivial problem, especially for geometry-complicated parts consisting of multiple freeform surfaces. Existing tool planning approaches are not able to deal with this kind of compound surfaces. This paper proposes a tool path planning approach which considers the tool motion performance and the thickness uniformity. There are two steps in this approach. The first step partitions the part surface into flat patches based on its topology and normal directions. The second step determines the tool movement patterns and the sweeping directions for each flat patch. Based on that, optimal tool paths can be calculated. Experimental tests are carried out on automotive body parts and the results validate the proposed approach.

This paper presents a distributed model for cooperative multiple mobile robot systems. In a multiple robot system, each mobile robot has sensing, computation and communication capabilities. The mobile robots spread out across certain area and share sensory information through an ad hoc wireless network. The multiple mobile robot system is therefore a mobile sensor network. In this paper, Voronoi diagram and Delaunay triangulation are introduced to model the area coverage and cooperation of mobile sensor networks. Based on the model, this paper discusses a fault tolerant algorithm for autonomous deployment of the mobile robots. The algorithm enables the system to reconfigure itself such that the area covered by the system can be enlarged. The proposed formation control algorithm allows the mobile sensor network to track moving target and sweep a larger area along specified paths.

Safety of road travel can be increased if vehicles can be made to form groups for mutual interaction with each other. With the number of sensors available on vehicles increasing, a need has arisen to make collaborative use of the information collected by individual vehicles to form an enhanced dataset to improve the inter-vehicular safety features. This paper presents a novel approach to increase the safety of road travel using the concept of wireless sensor networks. We discuss how a group of vehicles can form a mobile ad hoc network and exchange data sensed by the on-board sensors. The fusion of these data could give a better understanding of the surrounding traffic conditions. As the simulation results show, this would definitely increase the traveling safety on the road.

This paper presents an analysis and design method for human/robot integrated systems, especially fro the coordination of human and robot formations based on a group of distributed mobile manipulators. The key for the human/robot integrated system is to create a common motion reference that can be understood by both human and the robots in the formation. First, the perceptive reference frame is introduced and the characteristics of perceptive frame are compared with time based reference frame. Next, the stability of the system based on perceptive reference frame is investigated. The applications of perceptive reference frame to multi-agents coordination in a formation, human/mobile manipulators coordination are then discussed. Based on the perceptive reference frame, the human can be naturally integrated into the robot formation. Human intelligence can therefore be integrated with the mobility and dexterous manipulation capability of the mobile manipulators to undertake complex tasks. The robot formation is therefore able to reconfigure and thus cope with unexpected events. Experiments have been used to verify the theoretical results.

This paper describes a gait generation and control approach of a bipedal climbing robot with under-actuated mechanism. The special mechanical structure enables the robot to perform exploration tasks using "crawling", "pivoting" or "climbing" gaits. Multiple sensors are synthesized to generate successful gaits using a finite state machine. Experiments are conducted which demonstrate the effectiveness of proposed approach.

In order to execute complicated tasks involving contact with the environment, the robot manipulator must have force control capabilities. Many strategies for performing constrained motion control have been developed. The major drawback of these strategies is that they can only provide for the control of the interaction forces along fixed task space directions. However, many applications necessitate interaction force control along arbitrary and changing directions. In this paper, a new hybrid force/position controller, implemented in a moving reference frame, which can control the interaction force along arbitrary directions is proposed. Using the proposed controller the robot manipulator can interact with workpieces whose shape is not pre-specified. Thus tasks like milling, deburring, polishing and surface tracking, that involve constrained motion control of the robot manipulator can be performed on a wide variety of workpieces without explicit knowledge of the shape of the workpiece. The design of this new hybrid force/position controller, implemented in the moving hand coordinate frame, is presented and the controller is shown to be stable. Simulation studies for the task of tracking unknown surfaces are also presented.

A model identification method for unknown environments has been developed. By the interactions between a mobile manipulator and the unknown object, a nonholonomic cart, sensory information has been collected to estimate the model parameters of the cart, which are used to control the cart. Since the raw data are contaminated by noise they can not be modeled statistically, a wavelet based least square method is proposed to estimate these parameters for the cart. The raw signal has been decomposed into a certain bandwidth to generate a series of new signals, which are used to estimate the parameters. The new signal, which has the minimal estimation residual in least square sense is adopted as the best estimation. The error convergence of the estimation approach is given. The experimental results indicate that the estimation accuracy can be significantly improved by the use of the proposed method.

This paper presents an integrated task planning and control approach for manipulating a nonholonomic cart by mobile manipulators. The task considered in this study is to manipulate a nonholonomic cart to perform certain tasks such as pushing the cart along a straight line, making a turn at a corner, or tracking a sine wave. The cart manipulation task fully integrates the motion and force planning of the cart, and the planning and control of the mobile manipulators. The motion of the mobile manipulator and cart is coordinated by a common motion reference. The cart manipulating control has been implemented based on the decoupled mobile manipulation model and the force planning of the cart. The proposed integrated task planning and control approach enables the mobile manipulator complete complicated tasks by regulating its output force. The approach has been tested on a mobile manipulator consisting of a Nomadic XR4000 and a Puma 560 robot arm. The experimental results demonstrate the efficacy of this approach in the mobile manipulation of a nonholonomic cart.

This paper presents theoretical and experimental results on supermedia enhanced human/machine cooperative control of robot formations. Supermedia is the collection of all feedback streams rendered for the operator; such as video, haptic, temperature and others. The core idea is to utilize machine intelligence for the control of a robot formation. However, once this intelligence is insufficient to cope with unexpected events, human intervention is an option. To accomplish this, without the need for replanning, perceptive planning and control theory is utilized. This would allow the cooperation of human and machine for the control of the robot formation. To increase the flexibility and efficiency of such systems, commands of different levels of complexity can be issued. This gives rise to a hierarchical command structure, which can be described by a hierarchical perceptive frame that is modeled using automata and languages.

In this paper, a unified dynamic model for integrated mobile platform and on-board manipulator is developed. The mobile manipulator is considered as a redundant robot in the model. It provides a efficient and convenient framework to design a mobile manipulator controller as well as its action plans. Combing the event-based planning and control method with the nonlinear feedback technique, a task level action controller is designed. An online kinematic redundancy resolution scheme has also been developed. The system stability has been proven in the normal operation as well as in the case of appearance of unexpected obstacles. Furthermore, a robotic task involving both position and output force control of mobile manipulator can be easily planned. The proposed unified model approach has been implemented and tested on a mobile manipulator consisting of a Nomadic XR4000 and a Puma 560 robot arm. A cart pushing task is used to demonstrate the efficiency and effectiveness of the proposed approach.

The paper proposes, an integrated sensing and control framework for autonomous mobile manipulators. First, the mobile manipulator is considered as a redundant robot, while a unified dynamic model for an integrated mobile platform and on-board manipulator are developed. Combining the event-based planning and control method with a nonlinear feedback technique, a task level action controller is designed and an online kinematic redundancy resolution scheme is developed. Secondly, a force/torque sensor and a laser range sensor are used for the mobile manipulator to interact with objects and the environment. A nonholonomic cart pushing task has demonstrated the advantages of the integrated sensing and control approach of a mobile manipulator. The sensing and control approaches are tested on a mobile manipulator consisting of a Nomadic XR4000 and a Puma 560 robot arm.

This paper presents a hybrid system approach in the design of a singularityless task level controller. To achieve a singularityless motion control in the neighborhood of singularity, the hybrid system approach is used to integrate the task level controller and joint level controller. First, a hybrid system model is developed for the singularityless task level controller. A max-plus dynamic model is used to integrate the discrete switching control and continuous motion control in the controller. Based on this model, a smooth trajectory and control command for the hybrid system can be obtained. The Lyapunov theory was used to prove the stability of the singularityless controller. The new singularityless task level controller has been experimentally implemented and tested on a PUMA 560 robot manipulator. Experimental results have been employed to verify the theoretical conclusions, thus clearly demonstrating the advantages of the new task level control method.

The e-world, also known as the Internet, has added a new dimension to many of the traditional concepts in industrial applications and everyday life. The use of robots has dramatically expanded the potential of e-services. Individuals with particular expertise can perform highly accurate and fairly complicated tasks remotely via the Internet. This increase in the human reachability is faced by several obstacles. Reliable and efficient robot facilitated services via the Internet face several challenges. These range from human-computer interfacing and overcoming random time delay to task synchronization and human-robot interaction. These limitations intensify when many operators in many sites are involved. This paper provides new theoretical and experimental results on these challenges. Specifically, multisite cooperative control of an Internet based mobile manipulator is presented. The two main characteristics of this system are Internet based real-time closed loop control and coordinated operation. In addition, it is shown that despite random time delay the stability and synchronization of the system were achieved using event-based control.

A design method for non-time based tracking controller of mobile robots is presented. The new design method converts a controller designed by traditional time-based approaches to a non-time based controller using any given action reference. The stability condition of the non-time based tracking controller is developed and theoretically proved. The analysis and design methods are exemplified by a mobile robot tracking control problem. The controller has been implemented and tested in a Nomadic XR4000 mobile robot. The experimental results demonstrate the advantages of proposed method.