Two novel nonlinear pose (i.e., attitude and position) filters developed directly on the Special Euclidean Group SE(3) able to guarantee prescribed characteristics of transient and steady-state performance are proposed. The position error and normalized Euclidean distance of attitude error are trapped to arbitrarily start within a given large set and converge systematically and asymptotically to the origin from almost any initial condition. The transient error is guaranteed not to exceed a prescribed value while the steady-state error is bounded by a predefined small value. The first pose filter operates based on a set of vectorial measurements coupled with a group of velocity vectors and requires preliminary pose reconstruction. The second filter, on the contrary, is able to perform its function using a set of vectorial measurements and a group of velocity vectors directly. Both proposed filters provide reasonable pose estimates with superior convergence properties while being able to use measurements obtained from low-cost inertial measurement, landmark measurement, and velocity measurement units. The simulation results demonstrate the effectiveness and robustness of the proposed filters considering large error in initialization and high level of uncertainties in velocity vectors as well as in the set of vector measurements.

This paper formulates the attitude filtering problem as a nonlinear stochastic filter problem evolved directly on the Special Orthogonal Group 3 (SO(3)). One of the traditional potential functions for nonlinear deterministic complimentary filters is studied and examined against angular velocity measurements corrupted with noise. This paper demonstrates that the careful selection of the attitude potential function allows to attenuate the noise associated with the angular velocity measurements and results into superior convergence properties of estimator and correction factor. The problem is formulated as a stochastic problem through mapping SO(3) to Rodriguez vector parameterization. Two nonlinear stochastic complimentary filters are developed on SO(3). The first stochastic filter is driven in the sense of Ito and the second one considers Stratonovich. The two proposed filters guarantee that errors in the Rodriguez vector and estimates are semi-globally uniformly ultimately bounded in mean square. Simulation results are presented to illustrate the effectiveness of the proposed filters considering high level of uncertainties in angular velocity as well as body-frame vector measurements.

Combining spatial information with spectral information for classifying hyperspectral images can dramatically improve the performance. This paper proposes a simple but innovative framework to automatically generate spatial-spectral features for hyperspectral image classification. Two unsupervised learning methods-K-means and principal component analysis-are utilized to learn the spatial feature bases in each decorrelated spectral band. The spatial feature representations are extracted with these spatial feature bases. Then, spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. The experimental results indicate that the proposed method is flexible enough to generate rich spatial-spectral features and can outperform the other state-of-the-art methods.

Jointly combining the spatial and spectral features has proved to dramatically improve the performance of classifying hyperspectral image. Recently, utilizing neural networks to automatically model the spatial-spectral feature representations for hyperspectral images has become of great interest. This paper proposes a simple but innovative framework to classify hyperspectral image with two shallow convolutional neural networks (CNNs). First,principal component analysis whiteningis applied to decorrelate hundreds of spectral bands. Instead of selecting the principal components to reduce the spectral dimensionality, we retain all the spectral bands but compress the image cuboid into a one-channel spectral quilt bystacking spectral patches. In this way, not only all the spectral information is retained but also the computational complexity of training a neural network is reduced compared with conventional networks that directly input the spectral volumes. Moreover, the spectral quilt will contain some novel textural patterns that are effective at distinguishing classes. Two shallow CNNs are then applied to classify the spectral quilts. As shown in the experiments, both networks can outperform the standard analysis methods.

In this paper, we present the summary of results from a teleoperation study to assess the application of a mobile robot cornering law with the inclusion of a time delay on the returned video stream. The intent is to demonstrate this application to an analogous scenario like teleoperating from Earth a rover at the south Lunar pole. The first experiment compared course completion times for outdoor driving circuits in ideal lighting without time delay, ideal lighting with time delay, and in darkness with time delay and a low-angled spotlight. The second experiment studied cornering times for various time delays and lighting conditions in an indoor setting. The results show that teleoperating a mobile robot with the presence of time delay still complies with the previously developed cornering law. The combined results from the cornering study and the outdoor driving course are interpreted to show that the total time to complete a driving course with a time-delayed video can be predicted based on a known number of turns.

Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis-Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.

This paper presents a connection mechanism for autonomous self-assembly in mobile robots. Using this connection mechanism, mobile robots can be autonomously connected and disconnected. The purpose of self-assembly in mobile robotics is to add a new capability to mobile robots, thus, improving their performance to best fit the terrain conditions. Construction of a reconnectable joint is of primary concern in such systems. In this paper, first the geometric conditions and force equations of a general docking mechanism are studied. Then, we discuss the design details of our connection mechanism and present some experimental results that show that the proposed mechanism overcomes significant alignment errors and is considerably power efficient.

Two-dimensional or 3-D visual guidance is often used for minimally invasive cardiac surgery and diagnosis. This visual guidance suffers from several drawbacks such as limited field of view, loss of signal from time to time, and in some cases, difficulty of interpretation. These limitations become more evident in beating-heart procedures when the surgeon has to perform a surgical procedure in the presence of heart motion. In this paper, we propose dynamic 3-D virtual fixtures (DVFs) to augment the visual guidance system with haptic feedback, to provide the surgeon with more helpful guidance by constraining the surgeon's hand motions thereby protecting sensitive structures. DVFs can be generated from preoperative dynamic magnetic resonance (MR) or computed tomograph (CT) images and then mapped to the patient during surgery. We have validated the feasibility of the proposed method on several simulated surgical tasks using a volunteer's cardiac image dataset. Validation results show that the integration of visual and haptic guidance can permit a user to perform surgical tasks more easily and with reduced error rate. We believe this is the first work presented in the field of virtual fixtures that explicitly considers heart motion.

In this work, we propose a framework that can provide performance metrics with regards to the usability of an assistive device designed to provide visual servoing of a reaching task performed by a visually impaired individual. The framework provides a model and methodology from which performance metrics can be synthesized based on robotic manipular kinematics and an adaptation of Fitts' law for reaching task motions in a 3D environment. The purpose of which is to facilitate an understanding of how well the system's design accommodates the user's natural movement style when performing the reaching task. Use of advances in visual tracking techniques employing scale, view-point, and illumination invariant features are incorporated to allow for a further decoupling of the feature space from the joint-level control space.

The lack of a potential field model capable of providing accurate representations of objects of arbitrary shapes is considered one major limitation in applying the artificial potential field method in many practical applications. In this correspondence, we propose a potential function based on generalized sigmoid functions. The generalized sigmoid model can be constructed from combinations of implicit primitives or from sampled surface data. The constructed potential field model can achieve an accurate analytic description of objects in two or three dimensions and requires very modest computation at run time. In this correspondence, applications of the generalized sigmoid model in path-planning tasks for mobile robots and in haptic feedback tasks are presented. The validation results in this correspondence show that the model can effectively allow the user or mobile robot to avoid penetrations of obstacles while successfully accomplishing the task.

This paper investigates the inherent oscillation problem of potential field methods (PFMs) in the presence of obstacles and in narrow passages. These problems can cause slow progress and system instability in implementation. To overcome these two problems, in this paper, we propose a modification of Newton's method. The use of the modified Newton's method, which applies anywhere C/sub 2/ continuous navigation functions are defined, greatly improves system performance when compared to the standard gradient descent approach. To the best of our knowledge, ours is the first systematic approach to the oscillation problems in PFMs. We have validated this technique by comparing its performance with the gradient descent method in obstacle-avoidance tasks with different potential models and parameter changes.

In this work, we have taken the first step towards the creation of a computerized seeing-eye guide dog. The system we presented extends the development of assistive technology for the visually impaired into a new area: object tracking and visual servoing. The system uses computer vision to provide a kind of surrogate sight for the human user; sensing information from the environment and communicating it through haptic signalling. Our proof-of concept prototype is a low-cost wearable system which uses a colour camera to analyze a scene and recognize a desired object, then generate tactile cues to the wearer to steer his or her hand towards the object. We have proved the system in trials with random users in an unstructured environment.

In this paper, we propose a solution for robot motion planning in a complex environment with sparsely placed obstacles of arbitrary shape. We propose a generalized sigmoid function method to model the obstacles of arbitrary shape. The proposed model can achieve an accurate analytic description of the obstacles in two or three dimensions and is very easy to calculate and visualize. To demonstrate our generalized sigmoid method, we consider a robot team goal-finding task in this paper. Simulation results show that a mobile robot team can reach, the goal through the complex environment with obstacles of arbitrary shape without collision with any obstacles and other team members.

We consider potential field-based cooperative motion planning for a distributed team of semi-autonomous robots. We present a changing navigation function to allow the robots to incorporate new sensor data into their maps of the environment. We choose a Gaussian function to model attractors and a higher-order Gaussian-like function to model obstacles in order to avoid undesired local minima. Using arguments from hybrid systems theory, we show that this changing navigation function can be viewed as a mode-specific team Lyapunov function that stabilizes the system at all times. We. have verified our approach in simulations of a robot team mapping and foraging in an initially unknown environment. The team is able to map the environment, noting the location of all obstacles and attractive objects, then retrieve the attractors and return them to a goal position. Potential field navigation succeeds in this task while avoiding collisions between robots and obstacles as well as collisions among team members.

We investigate issues of control and motion planning for a biomimetic robotic system. Previous work has shown that a successful approach to solving the motion planning problem is to decouple it into the two subproblems of trajectory generation (feedforward controls) and feedback regulation. In this paper, we investigate basic issues of momentum generation for a class of dynamic mobile robots, focusing on eel-like swimming robots. We develop theoretical justification for a forward gait that has been observed in nature, and for a turning gait, used in our control laws, that has not been extensively studied in the biological literature. We also explore theoretical predictions for novel gaits for turning and sideways swimming. Finally, we present results from experiments in motion planning for a biomimetic robotic system. We show good agreement with theory for both open and closed-loop control of our modular, five-link, underwater swimming robot using image-based position sensing in an aquatic environment.

We investigate basic issues of motion planning for a class of dynamic mobile robots, focusing on eel-like swimming robots. A primary characteristic of this class of robots is that drift plays a significant role in the generation of motion. We provide a brief summary of our previous work in eel-like robotics, in which we explored generic gait patterns that could be used to drive an eel-like robot. We make an analogy with kinematic car-like robots to develop feedback algorithms to perform trajectory trucking about a nominal path. We use these control laws in closed-loop experiments with an underwater eel-like robot, using a vision based position sensing system to provide feedback.

We investigate basic issues of motion planning for a class of dynamic mobile robots, focusing on eel-like swimming robots. A primary characteristic of this class of robots is that drift plays a significant role in the generation of motion. We build on previous work (1999) in which we explored generic gait patterns that could be used to drive an eel-like robot. We make an analogy with kinematic car-like robots to develop a nominal path from an initial state to a goal state, and then develop feedback algorithms to perform trajectory tracking around this nominal path. We also address the central issues that arise when using cyclic gaits as the basis for control strategies.

We propose a hierarchical structure for control of autonomous robots. We draw on ideas from the hybrid systems theory, which describe systems governed by a discrete set of continuous modes of operation and transitions between these modes. We aim to enable rapid development of test applications and to allow easy performance upgrades. Our structure is based on the creation of agents to process inputs and outputs at the highest level of abstraction in the system (the application domain). We describe our implementation of this structure in two systems: a nonholonomic car-like robot and a quadrupedal entertainment robot. Both examples demonstrate how abstraction of planning away from the implementation details of the robot enables rapid development.

We investigate issues of control and motion planning for a biomimetic robotic system. Previous work has shown that the motion problem can be decoupled into trajectory generation and steering. We investigate basic issues of momentum generation for a class of dynamic mobile robots, focusing on eel-like swimming robots. We use control laws based on a series of gaits motivated by the biological literature on coupled oscillators and central pattern generators. A primary characteristic of this class of robots is that drift plays a significant role in the generation of motion. We develop theoretical justification for a forward gait that has been observed in nature and for a turning gait, used in our control laws, that has not yet been studied in the biological literature. We also explore theoretical predictions of novel gaits for turning and sideways swimming.

In this paper, we approach the modeling, simulation, and control of snake and eel-like robots from a geometric perspective. We propose basic viscous and fluid drag models to capture the effect of external forces acting on the bodies of land-based snakes and aquatic eels, respectively. Using these models, we show that the dynamics can be decomposed into motions in the position and orientation of the overall system and changes in its internal shape. This allows us to write reduced dynamics for the serpentine and swimming motions that isolate the effect of shape changes on the momentum of the system. We explore in simulation several types of locomotive gaits, and report qualitative experimental results that provide validation for these simulations.