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Redundant space manipulators, including Space Station Remote Manipulator System (SSRMS), Special Purpose Dexterous Manipulator (SPDM) and European Robotic Arm (ERA), have been playing important roles in the construction and maintenance of International Space Station (ISS). They all have 7 revolute joints arranged in similar configurations, and are referred to as SSRMS-type manipulators. When a joint is locked in an arbitrary position due to some failures, a 7R manipulator degrades to a 6R manipulator. Without a spherical wrist or three consecutive parallel joints, the inverse kinematics of the 6R manipulator is very complex. In this paper, we propose effective methods to resolve the inverse kinematics for different cases of any joint locked in an arbitrary position. Firstly, configuration characteristics of the SSRMS-type redundant manipulators are analyzed. Then, an existing of closed-form inverse kinematics is discussed for locking different joints. Secondly, D–H frames and corresponding D–H parameters of the new 6-DOF manipulator formed by locking a joint in an arbitrary position are re-constructed. A unified table is then created to describe the kinematics for all possible cases of single joint locking failure. Thirdly, completely analytical and semi-analytical methods are presented to solve the inverse kinematics equations, and the former is used for locking joint 1, 2, 6 or 7 while the latter for locking joint 3, 4 or 5. Finally, typical cases for single joint locking are studied. The results verify the proposed methods.

In this paper, we study the parameter setting for a set of intelligent multi-category classifiers in wall-following robot navigation. Based on the swarm optimization theory, a particle selecting approach is proposed to search for the optimal parameters, a key property of this set of multi-category classifiers. By utilizing the particle swarm search, it is able to obtain higher classification accuracy with significant savings on the training time compared to the conventional grid search. For wall-following robot navigation, the best accuracy (98.8%) is achieved by the particle swarm search with only 1/4 of the training time by the grid search. Through communicating the social information available in particle swarms in the training process, classification-based learning can achieve higher classification accuracy without prematurity. One of such learning classifiers has been implemented in SIAT mobile robot. Experimental results validate the proposed search scheme for optimal parameter settings.

Abnormal crowd behavior detection plays an important role in surveillance applications. We propose a camera parameter independent and perspective distortion invariant approach to detect two types of abnormal crowd behavior. The two typical abnormal activities are people gathering and running. Since people counting is necessary for detecting the abnormal crowd behavior, we present an potential energy-based model to estimate the number of people in public scenes. Building histograms on the X- and Y-axes, respectively, we can obtain probability distribution of the foreground object and then define crowd entropy. We define the Crowd Distribution Index by combining the people counting results with crowd entropy to represent the spatial distribution of crowd. We set a threshold on Crowd Distribution Index to detect people gathering. To detect people running, the kinetic energy is determined by computation of optical flow and Crowd Distribution Index. With a threshold, kinetic energy can be used to detect people running. To test the performance of our algorithm, videos of different scenes and different crowd densities are used in the experiments. Without camera calibration and training data, our method can robustly detect abnormal behaviors with low computation load.

Space robotic systems are expected to play an increasingly important role in future space activities. Nevertheless, dynamics modeling and motion planning of a space robot are much more complex than those of a fixed-base robot, due to the dynamic coupling between the manipulator and its base. On the other hand, in order to assure the success of on-orbital missions, many experiments are required to verify the key algorithms on the ground before the space robot is launched. In this paper, the main research achievements on dynamics modeling, path planning, and ground verification are reviewed, and future studies are recommended. Firstly, we summarize the essential modeling concepts, and deduce the kinematics and dynamics equations of a space robot. Secondly, the main motion planning approaches are discussed. Then, different ground verification systems, including the air-bearing table, neutral buoyancy, airplane flying, free-falling motion, suspension system, and hybrid system, are introduced. Finally, the future research trends are forecasted.

Space robotic systems are expected to play an increasingly important role in the future. However, the control methods based on the inverse kinematics are affected by singularities. In this paper, practical approaches are proposed to solve the problems of a wrist-partitioned space manipulator. For spacecraft-referenced end-point motion control, we presented the singularity separation plus damped reciprocal (SSPDR) method, which separates the singularity parameters from the inverse of the Jacobian, and replaces their reciprocals using the damped reciprocals. For another control strategy, i.e. inertially referenced end-point motion control, including spacecraft attitude-controlled mode and free-floating mode, the linear momentum equation is used to eliminate three independent variables. With modifying some expressions, the SSPDR method is utilized to solve the singularities of spacecraft attitude-controlled space robot. When the space robot is free-floating, the singularities, i.e. the so-called dynamic singularities, cannot be predicted according to its kinematic structure. Combining with the measured angular velocity of the base, the dynamic singularity handling problem is transformed into real-time kinematic singularity avoiding problem, which can be solved by the SSPDR method. Since the SVD decomposition, the estimation of the minimum singularity value, and the calculation of the generalized Jacobian matrix are not required, the algorithm has lower computation load. Another advantage is that, only the accuracy of part velocity components is reduced by adding the damped coefficients. Simulation results verify the proposed approaches.

Denoising of electrocardiogram (ECG) is the fundamental technique for manual or automatic ECG diagnosis. Model-based denoising has attracted initial studies since the ECG dynamical model was established in 2003 and been demonstrated to outperform most model-less denoising methods. The focus of this paper is robust denoising of abnormal ECG signals, which do not satisfy the assumption in previous model-based studies that morphological or physiological variations are small from one beat to another. A mean shift based initializer is proposed to provide a much more robust estimation of initial model parameters for each heart beat. Together with physiological knowledge based wave sub-segmentation and enhanced strategies, the novel initializer has been demonstrated to achieve satisfactory performance for both normal and abnormal heart beats under both white and pink noises. Utilizing records from Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) database, this paper also applies various filters to denoise noisy signals and the denoising performances verify the availability and efficacy of the proposed denoising method.

Space robots are playing an increasingly important role in on-orbital servicing, including repairing, refueling, or de-orbiting the satellite. The target must be captured and berthed before the servicing task starts. However, the attitude of the base may lean much and needs re-orientating after capturing. In this paper, a method is proposed to berth the target, and re-orientate the base at the same time, using manipulator motion only. Firstly, the system state is formed of the attitude quaternion and joint variables, and the joint paths are parameterized by sinusoidal functions. Then, the trajectory planning is transformed to an optimization problem. The cost function, defined according to the accuracy requirements of system variables, is the function of the parameters to be determined. Finally, we solve the parameters using the particle swarm optimization algorithm. Two typical cases of the spacecraft with a 6-DOF manipulator are dynamically simulated, one is that the variation of base attitude is limited; the other is that both the base attitude and the joint rates are constrained. The simulation results verify the presented method.

This paper presents a new method for fault diagnosis using a newly developed method, support vector machine (SVM). First, the basic theory of the SVM is briefly reviewed. Next, a fast implementation algorithm is given. Then the method is applied for the fault diagnosis in sheet metal stamping processes. According to the tests on two different examples, one is a simple blanking and the other is a progressive operation, the new method is very effective. In both cases, its success rate is over 96.5%. In comparison, the success rate of the popular artificial neural network (ANN) is just 93.3%. In addition, the new method requires only few training samples, which is an attractive feature for shop floor applications.

Finite-time control of the robot system is studied through both state feedback and dynamic output feedback control. The effectiveness of the proposed approach is illustrated by both theoretical analysis and computer simulation. In addition to offering an alternative approach for improving the design of the robot regulator, this research also extends the study of the finite-time control problem from second-order systems to a large class of higher order nonlinear systems.

Modeling human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics and intelligent vehicle highway systems to expert training and virtual reality computer games. Usually, HCS models are derived empirically, rather than analytically, from real-time human input–output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, there is a great need to develop adequate performance criteria for these models. It is our goal in this paper to develop several such criteria for the task of human driving. We first collect driving data from different individuals through a real-time graphic driving simulator that we have developed, and identify each individual’s control strategy model through the flexible cascade neural network learning architecture. We then define performance measures for evaluating two aspects of the resultant HCS models. The first is based on event analysis, while the second is based on inherent analysis. Using the proposed performance criteria, we demonstrate the procedure for evaluating the relative skill of different HCS models. Finally, we propose an iterative algorithm for optimizing an initially stable HCS model with respect to independent, user-specified performance criteria, by applying the simultaneously perturbed stochastic approximation (SPSA) algorithm. The methods proposed herein offer a means for modeling and transferring HCS in response to real-time inputs, and improving the intelligent behaviors of artificial machines.

Owing to the ever-increased demand for product quality improvement and production cost reduction, on-line monitoring of stamping operations has become a common practice in shop floors around the world today. Based on a market survey, for most stamping processes, monitoring systems use tonnage signals and/or strain signals. Recently, a few attempts have been reported using acceleration signals as they contain rich information and are relatively inexpensive. However, it is known that acceleration signals are vulnerable to the noise disturbances, and hence are less robust. This paper presents a study that uses bispectrum to analyze the acceleration signals. It is shown that the bispectrum can suppress Gaussian color noise to boost the signal-to-noise ratio. It also extracts the features of the signal that are related to the defective parts (such as material too thick or slug). The experimental results demonstrate that the method presented is effective and has a good potential for applications in shop floor. We also present new method for reducing the computation load in the process.

The fault diagnosis is important in continuously monitoring the performance and quality of manufacturing processes. Overcoming the drawbacks of threshold approach, artificial neural network may extract the symptom of the faults through learning from the samples, but it is difficult to design its structure. Moreover, it needs a large numbers of samples in practice. In this paper, support vector machine approach was proposed to overcome these limitations based on statistics learning theory, and a new fault diagnosis system is developed. The experimental results showed that it is an efficient and practical on-line intelligent monitoring system for the stamping processes.

A single wheel, gyroscopically stabilized robot was developed to provide a dynamic stability for rapid locomotion. It is a sharp-edged wheel actuated by a spinning flywheel for steering and a drive motor for propulsion. The spinning flywheel acts as a gyroscope to stabilize the robot and it can be tilted to achieve steering. Its nature is nonholonomic, underactuated and inherently unstable in the lateral direction. In this paper, we discuss the problem of controlling the tilt-up motion of such a dynamically stable but statically unstable robot. We first present a dynamic model which is verified through simulations and experiments. Then, we develop a model-based controller for tilt-up motion, assuming that the robot is rolling without slipping. Taking advantage of human skill in teleoperating the robot, we develop a human-based controller for tilt-up motion, in which we train a human control strategy model to abstract the operator’s skill in controlling the tilt-up motion. The controller was successfully implemented, such that the robot can recover from fall automatically. This work is a significant step towards a fully automatic control of such a dynamically stable but statically unstable robot.

In this article we present a hybrid systems approach for fault tolerant control of robot manipulators. Our method is based on a combination of fault detection and isolation, control reconfiguration, and control algorithms. The contribution of this work is the construction of a hybrid automaton for control reconfiguration, whose discrete states indicate the number of failed joints, and whose dynamic states are the positions of the joints. With thie proposed method, it is possible to make the manipulator’s end-effector reach any Cartesian configuration inside its workspace irrespective of the number of joint failures. This is a significant property for manipulators operating in environments where actuator repair is too costly or impossible, such as in space or deep-sea. Experimental results with a 3-link planar manipulator illustrate the proposed method.

The underactuated free floating robot in space is nonlinear systems where velocity and acceleration constraints are both nonintegrable, therefore it is a secondorder nonholonomic system. Some of the existing nonholonomic control methods will not be directly applicable to such systems as it is extremely difficult, if not impossible, to find the control Lie brackets. In this paper, by investigating the system dynamics in depth, a simple velocity-based method is proposed to control the unactuated joints and a multi-step composite strategy to implement orientation tracking tasks. The proposed algorithm is of significance in controlling of space robots when some joints fail to function, or they are intentionally set to be passive for energy efficiency and safety purposes.

We have developed a light-weight space manipulator, Self-Mobile Space Manipulator (SM'), in the Robotics Institute at Carnegie Mellon University. SM' is a 7-degreeof-freedom (DOF), 1/3-scale, laboratory version of a robot designed to walk on the trusswork and other exterior surfaces of Space Station Freedom, and to perform manipulation tasks that are required for inspection, maintenance, and construction. Combining the mobility and manip ulation functions in one body as a mobile manipulator, SM2 is capable of routine tasks such as inspection, parts transportation, object lighting, and simple assembly procedures. The system will provide assistance to astronauts and greatly reduce the need for astronaut extra-vehicular activity (EVA). This paper discusses the robot hardware development, gravity compensation system, control structure and teleoperation functions of the SM2 system, and demonstrates its capabilities of locomotion and manipulation in space applications.

Inverse kinematics is computational expensive, and for some cases requires numerical solution. Especially for a redundant robot, the inverse kinematic mapping has to be done using a task and/or environmental model and an optimization scheme. This results in inefficient computation and control delay in real-time implementation. In this paper, we propose a fuzzy logic approach for the inverse kinematic mapping which is independent of the number of links, and thus is feasible for both non-redundant and redundant robots. The approach produces zero steady-state error and can be carried out efficiently. The feasibility of this approach has been demonstrated by implementing it in the real-time teleoperation control of a seven-joint space robot.

This paper addresses the problem of planning the motion of a polygonal object through a set of planar obstacles. We propose a two-disk motion planning strategy to navigate the object within the free space between the obstacles from an initial location to a final location. This method makes use of the Medial Axis Transform (MAT) of the free space which can be generated efficiently using the method developed in [11]. We determine two minima] overlapping disks that fully enclose the moving object, and then constrain the centers of the two disks to move continuously aJong a path on the medial axis. In this paper we direct our efforts to the problem of finding the two enclosing disks for a moving object which is considered as a polygon. The problem has been considered as being optimally cutting a polygon into two smaller polygons such that each of smaller polygons can be covered by a minimal disk. We have proved that if the cut is optimal. the resultant minimal disks for two smaller polygons have equal diameters. Based on the geometry of a convex polygon, we formulated the problem as an optimization problem to determine a local optimum for a given edge pair. For a concave polygon, we propose a method to create a hypothetical convex polygon for approximating a given concave polygon, and then perform optimization using the method described above. We have proved that the maximal radii of the disks covering the hypothetical convex polygon generated from a concave polygon is 1.25 times of the radii of the disks that covers the original concave polygon. This shows that the approximation of a concave polygon using a minimal convex polygon does not yield too conservative solution. Simulations are presented for a variety of polygons. The method is being used for disassembly motion planning of a subassembly within its parent subassembly.