Understanding and mimicking human gait is essential for design and control of biped walking robots. The unique characteristics of normal human gait are described as passive dynamic walking, whereas general human gait is neither completely passive nor always dynamic. To study various walking motions, it is important to quantify the different levels of passivity and dynamicity, which have not been addressed in the current literature. In this paper, we introduce the initial formulations of Passive Gait Measure (PGM) and Dynamic Gait Measure (DGM) that quantify passivity and dynamicity, respectively, of a given biped walking motion, and the proposed formulations will be demonstrated for proof-of-concepts using gait simulation and analysis. The PGM is associated with the optimality of natural human walking, where the passivity weight functions are proposed and incorporated in the minimization of physiologically inspired weighted actuator torques. The PGM then measures the relative contribution of the stance ankle actuation. The DGM is associated with the gait stability, and quantifies the effects of inertia in terms of the Zero-Moment Point and the ground projection of center of mass. In addition, the DGM takes into account the stance foot dimension and the relative threshold between static and dynamic walking. As examples, both human-like and robotic walking motions during single support phase are generated for a planar biped system using the passivity weights and proper gait parameters. The calculated PGM values show more passive nature of human-like walking as compared with the robotic walking. The DGM results verify the dynamic nature of normal human walking with anthropomorphic foot dimension. In general, the DGMs for human-like walking are greater than those for robotic walking. The resulting DGMs also demonstrate their dependence on the stance foot dimension as well as the walking motion; for a given walking motion, smaller foot dimension results in increased dynamicity. Future work on experimental validation and demonstration will involve actual walking robots and human subjects. The proposed results will benefit the human gait studies and the development of walking robots.

In this work, an optimal maneuverability strategy for car-like unmanned vehicles operating in restricted environments is presented. The maneuverability strategy is based on a path planning algorithm that uses the environment information to plan a safe, feasible and optimum path for the unmanned mobile robot. The environment information is obtained by means of a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm uses the sensors' information to build a map of the surrounding environment. A Monte Carlo sampling technique is used to find an optimal and safe path within the environment based on the SLAM information. The objective of the planning is to safely reach a desired orientation in a bounded space. Theoretical demonstrations and real-time experimental results (in indoor and outdoor environments) are also presented in this work.

The teaching of how to exert fingertip forces and how to move the fingers is essential for transferring skill using the fingers to perform fine motor tasks. In this paper we accomplish the transfer of fingertip forces and positions in three-dimensional space by combining a multi-fingered haptic interface robot, which can measure and present the three-dimensional forces and positions at five fingertips, and an image display system that records a trainer's hand image and displays it to a trainee. Several experimental results show a high fingertip force and position transferability and the great potential of our proposed transfer system.

Discrete kinematic synthesis of discretely actuated hyper-redundant manipulators is a new practical problem in robotics. The problem concerns with determining the type of each manipulator module from among several specific types, so that the manipulator could reach several specified target frames with the lowest error. This paper suggests using a breadth-first search method and a workspace mean frame to solve this problem. To reduce errors, two heuristic ideas are proposed: two-by-two searching method and iteration. The effectiveness of the proposed method is verified through several numerical problems.

This paper presents an approach to visual obstacle avoidance and reactive robot navigation for outdoor and indoor environments. The obstacle detection algorithm includes an image feature tracking procedure followed by a feature classification process based on the IPT (Inverse Perspective Transformation). The classifier discriminates obstacle points from ground points. Obstacle features permit to draw out the obstacle boundaries which are used to construct a local and qualitative polar occupancy grid, analogously to a visual sonar. The navigation task is completed with a robocentric localization algorithm to compute the robot pose by means of an EKF (Extended Kalman Filter). The filter integrates the world coordinates of the ground points and the robot position in its state vector. The visual pose estimation process is intended to correct possible drifts on the dead-reckoning data provided by the proprioceptive robot sensors. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal has proved to be useful, and show, both qualitatively and quantitatively, the benefits it provides.

The paper examines the problem of dynamic ship positioning with the use of Kalman Filter- and Particle Filter-based sensor fusion algorithms. The proposed approach enables to estimate accurately the ship's state vector by fusing the vessel's position and heading measurements coming from on-board sensors together with distance measurements coming from sensors located at the coast (e.g. radar). The estimated state vector is used in turn, in a control loop, to regulate the horizontal position and heading of the vessel. The performance of dynamic positioning of the ship based on Kalman and Particle Filtering is evaluated through simulation experiments.

This paper investigates algorithms for enabling surgical slave robots to autonomously explore shape and stiffness of surgical fields. The paper addresses methods for estimating shape and impedance parameters of tissue and methods for autonomously exploring perceived impedance during tool interaction inside a tissue cleft. A hybrid force-motion controller and a cycloidal motion path are proposed to address shape exploration. An adaptive exploration algorithm for segmentation of surface features and a predictor-corrector algorithm for exploration of deep features are introduced based on discrete impedance estimates. These estimates are derived from localized excitation of tissue coupled with simultaneous force measurements. Shape estimation is validated in ex-vivo bovine tissue and attains surface estimation errors of less than 2.5 mm with force sensing resolutions achievable with current technologies in minimally invasive surgical robots. The effect of scan patterns on the accuracy of the shape estimate is demonstrated by comparing the shape estimate of a Cartesian raster scan with overlapping cycloid scan pattern. It is shown that the latter pattern filters the shape estimation bias due to frictional drag forces. Surface impedance exploration is validated to successfully segment compliant environments on flexible inorganic models. Simulations and experiments show that the adaptive search algorithm reduces overall time requirements relative to the complexity of the underlying structures. Finally, autonomous exploration of deep features is demonstrated in an inorganic model and ex-vivo bovine tissue. It is shown that estimates of least constraint based on singular value decomposition of locally estimated tissue stiffness can generate motion to accurately follow a tissue cleft with a predictor-corrector algorithm employing alternating steps of position and admittance control. We believe that these results demonstrate the potential of these algorithms for enabling “smart” surgical devices capable of autonomous execution of intraoperative surgical plans.

We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained.

This paper presents an indirect adaptive fuzzy control scheme for a class of unknown multi-input multi-output (MIMO) nonlinear systems with external disturbances. Within this scheme, the dynamic fuzzy logic system (DFLS) is employed to identify the unknown nonlinear MIMO systems. The control law and parameter adaptation laws of DFLS are derived based on the Lyapunov synthesis approach. The control law is robustified in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions it guarantees the boundness of all signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. An extensive simulation on the tracking control of a two-link rigid robotic manipulator verifies the effectiveness of the proposed algorithms.

Safe navigation of robotic vehicles is considered as a key pre-requisite of successful mission operations within highly adverse and unconstrained environments. While there has been extensive research in the perception of positive obstacles, little progress can be accredited to the field of negative obstacles. This paper hypostatizes an elaborative attempt to address the problem of negative obstacle detection and traversability analysis in the form of gaps by processing 3-dimensional range data. The domain of application concerns Urban Search and Rescue scenarios that reflect environments of increased complexity in terms of diverse terrain irregularities. To allow real-time performance and, in turn, timely prevention of unrecoverable robotic states, the proposed approach is based on the application of efficient image morphological operations for noise reduction and border following the detection and grouping of gaps. Furthermore, we reason about gap traversability, a concept that is novel within the field. Traversability assessments are based on features extracted through Principal Component Analysis by exploring the spatial distribution of the interior of the individual gaps or the orientation distribution of the corresponding contour. The proposed approach is evaluated within a realistic scenario of a tunnel car accident site and a challenging outdoor scenario. Using a contemporary Search and Rescue robot, we have performed extensive experiments under various parameter settings that allowed the robot to always detect the real gaps, and either optimally cross over those that were traversable or otherwise avoid them.

Tipping-over and slipping, which are related to zero moment point (ZMP) and frictional constraint respectively, are the two most common instability forms of biped robotic walking. Conventional criterion of stability is not sufficient in some cases, since it neglects frictional constraint or considers translational friction only. The goal of this paper is to fully address frictional constraints in biped walking and develop corresponding stability criteria. Frictional constraints for biped locomotion are first analyzed and then the method to obtain the closed-form solutions of the frictional force and moment for a biped robot with rectangular and circular feet is presented. The maximum frictional force and moment are calculated in the case of ZMP at the center of contact area. Experiments with a 6-degree of freedom active walking biped robot are conducted to verify the effectiveness of the stability analysis.

This paper introduces the design, analysis, and experimental results of a fast mesoscale (12 cm length) quadruped mobile robot that employs unconventional actuators. Four legs of the robot are actuated by two pieces of piezocomposite actuator named LIPCA, which enables the robot to achieve the bounding gait with only one degree of freedom per leg. The forward locomotion is obtained by a creative idea in the design and the speed can be controlled by changing the frequency of actuators. The mechanism of power transfer has been improved in order to use the actuation power more efficiently. Two small RC-servo motors are added to control the locomotion direction. In addition, a small power supply and control circuit is developed that is fit for the robot. Our experiments show that the robot can locomote as fast as about two times its body length per second with the circuit board and a battery installed. The robot is also able to change the heading direction in a controlled way and is capable of continuous operation for 35 min.

This paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on the initial task allocation using a contract net-based approach, a new method to select the negotiation robots and construct the negotiation set is proposed by employing the utility functions. A negotiation mechanism suitable for the decentralized task allocation is also presented. Then, a game theory-based negotiation strategy is proposed to achieve the Pareto-optimal solution for the task reallocation. Extensive simulation results are provided to show that the task allocation solutions after the negotiation are better than the initial contract net-based allocation. In addition, experimental results are further presented to show the effectiveness of the approach presented.

A novel path planner is presented for the local path planning of a single robot (represented with R) in a complicated dynamic environment. Here a series of attractive points are computed based on attractive segments for guiding R to move along a shorter path. Each attractive segment is obtained by using the full environmental knowledge and will be used for several sampling times in general. A motion controller, which is designed based on artificial moments and a robot model that has a principal motion direction line(PMDline), makes R move closely to attractive points while away from obstacles. Attractive and repulsive moments are designed, which only make R's PMDline face toward attractive points and opposite to obstacles in general, as in most cases, R will move along its PMDline with its full speed. Because of the guidance of attractive points and R's full-speed motion, the global convergence is guaranteed. Simulations indicate that the proposed path planner meets the requirements of real-time property while can optimize R's traveling path.

There is an increasing popularity of mobile robot applications over the Internet. Robots need some kind of wireless communication to receive commands and transmit information to users. This paper describes the problems encountered to keep the mobile robots connected and the solutions adopted. The first problem was related to delays and the very low throughput that occur when the robot roams to a new access point. Most commercial systems currently installed implement proprietary solutions with different behaviors. Here, a simple and device-independent solution for mobile robot applications is proposed. The second problem is the lack of wireless coverage in some areas of buildings. The solutions adopted for these problems are based on coverage maps.

Clinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subject's safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

This paper presents a novel line of sight control system for a robot vision tracking system, which uses a position feedforward controller to preposition a camera, and a vision feedback controller to compensate for the positioning error. Continuous target tracking is an important function for service robots, surveillance robots, and cooperating robot systems. However, it is difficult to track a specific target using only vision information, while a robot is in motion. This is especially true when a robot is moving fast or rotating fast. The proposed system controls the camera line of sight, using a feedforward controller based on estimated robot position and motion information. Specifically, the camera is rotated in the direction opposite to the motion of the robot. To implement the system, a disturbance compensator is developed to determine the current position of the robot, even when the robot wheels slip. The disturbance compensator is comprised of two extended Kalman filters (EKFs) and a slip detector. The inputs of the disturbance compensator are data from an accelerometer, a gyroscope, and two wheel-encoders. The vision feedback information, which is the targeting error, is used as the measurement update for the two EKFs. Using output of the disturbance compensator, an actuation module pans the camera to locate a target at the center of an image plane. This line of sight control methodology improves the recognition performance of the vision tracking system, by keeping a target image at the center of an image frame. The proposed system is implemented on a two-wheeled robot. Experiments are performed for various robot motion scenarios in dynamic situations to evaluate the tracking and recognition performance. Experimental results showed the proposed system achieves high tracking and recognition performances with a small targeting error.

In this paper, Clifford Algebra is used to model and facilitate solving the inverse kinematic problem for robots with only two consecutive parallel axes. It is shown that when a solution exists, it is usually the case that one of the angles of rotation can be arbitrarily chosen from a union of intervals. The remaining angles are then uniquely determined. Of course, there are cases when no solution exists, such as when the object is out of reach. But typically, when solutions exist, there are infinitely many sets of solutions.

In this work, a generalized adaptive control scheme for the global position stabilization of robot manipulators with bounded inputs is proposed. It gives rise to various families of bounded controllers with adaptive gravity compensation. Compared with the adaptive approaches previously developed in a bounded-input context, the proposed scheme guarantees the adaptive regulation objective: globally, avoiding discontinuities in the control expression as well as in the adaptation auxiliary dynamics, preventing the inputs to reach their natural saturation bounds, and imposing no saturation-avoidance restriction on the control gains. Experimental results corroborate the efficiency of the proposed adaptive scheme.

A novel parallel hip joint simulator, called 3SPS+1PS bionic parallel test platform, with 4 degrees of freedom including three rotations and one translation is designed to represent three-dimensional motion and compound friction movement of a human hip joint and to be a better simulator for testing the tribology performance of biomaterials for hip joint prosthesis. Stiffness is one of the most important performances of parallel manipulators, as well as for the 3SPS+1PS parallel manipulator with higher speeds. First, the differential kinematic/static model was derived based on the kinematics model. The relationship between the elastic deformation of each active leg and the variation of position/orientation deformation of the moving platform was described based on the virtual work principle. Then, a 6 × 6 global stiffness matrix of the 3SPS+1PS parallel manipulator was derived. The maximum versus minimum eigenvalues of the global stiffness matrix were obtained as its two evaluation indexes. By letting the 3SPS+1PS bionic parallel test platform represent three rotation motions and the dynamic loading of the human hip joint as described by ISO 14242 Part-1, the forces acted on each active leg and their responding elastic deformations were analyzed. The distributions for maximum and minimum stiffness in different workspace were detected. Finally, the results showed that the minimum stiffness in the whole workspace should be larger than the allowable stiffness of the 3SPS+1PS parallel manipulator.