Many recent developments of surgical robots focus on less invasive paradigms, such as laparoscopic SPA (Single Port Access) surgery, NOTES (Natural Orifice Translumenal Endoscopic Surgery), laryngoscopic MIS (Minimally Invasive Surgery), etc. A configuration similarity shared by these surgical robots is that two or more manipulators are inserted through one access port (a laparoscope, an endoscope, or a laryngoscope) for surgical interventions. However, upon designing such a surgical robot, the structure of the inserted manipulators has not been thoroughly explored based on evaluation of their performances. This paper presents a comparison for kinematic performances among three different continuum manipulators. They all could be applied in the aforementioned surgical robots. The structural parameters of these continuum manipulators are firstly optimized to assure a more fair and consistent comparison. This study is conducted in a dimensionless manner and provides scalable results for a wide spectrum of continuum manipulator designs as long as their segments have a constant curvature. The results could serve as a design reference for future developments of surgical robots which use one access port and continuum mechanisms.

This special issue is associated with the 22nd International Workshop on Robotics in the Alpe-Adria-Danube Region (RAAD2013) that took place on September 11 to 13, 2013 in Portorož, Slovenia. The RAAD conference series was established in 1992 (initially under the name RAA). At that time, RAAD played an important role towards enhancing international cooperation among neighborhood countries, while addressing the evolution of robotics from classical industrial applications to novel challenging areas such as service and humanoid robotics. Although RAAD conference got its name from a geographical region (Alpe-Adria-Danube), it was never meant to be limited to participants coming solely from this region. This aspect has grown through the years and in 2013 RAAD has got 50 papers from authors who came from 20 different countries. Invited plenary lectures have been delivered by Prof. Auke Ijspeert from EPFL (Switzerland), Prof. Alin Albu-Schäffer from DLR (Germany), and prof. I-Ming Chen from Nanyang Technological University (Singapore).

This paper presents a method of planning a sub-optimal trajectory for a mobile manipulator subject to mechanical and control constraints. The path of the end-effector is defined as a curve that can be parameterised by any scaling parameter—the reference trajectory of a mobile platform is not needed. Constraints connected with the existence of mechanical limits for a given manipulator configuration, collision avoidance conditions and control constraints are considered. Nonholonomic constraints in a Pfaffian form are explicitly incorporated to the control algorithm. To avoid manipulator singularities, the motion of the robot is planned in order to maximise the manipulability measure.

This paper aims to represent a human's upper body motion using a humanoid robot. As a robot has different physical limitations than a human, we present a method that can filter the trajectories to meet the limitations. The filtering can be used directly and also can be used as a constraint for optimization. It will be shown to be applicable both offline and online. Many physical attributes, namely angle, collision, velocity, and dynamic torque, are represented as B-spline explicit functions. The B-spline coefficients are then calculated to limit the physical attributes. This explicit method can guarantee that many limits are met. Hence, it is better than many methods that only reduce such physical attributes using objective functions.

Verbal communication is the most natural way of human–robot interaction. Such an interaction is usually achieved by means of a human-robot interface (HRI). In this paper, a HRI is presented to teleoperate a robotic platform via the user's voice. Hence, a speech recognition system is necessary. In this work, a user-dependent acoustic model for Spanish speakers has been developed to teleoperate a robot with a set of commands. Experimental results have been successful, both in terms of a high recognition rate and the navigation of the robot under the control of the user's voice.

This paper discusses a planar 2-DOF (degrees of freedom) parallel kinematic machine with actuation redundancy. Its inverse dynamic model is constructed by utilizing the Newton–Euler method based on the kinematic analysis. However, the dynamic model cannot be solved directly because the number of equations is less than the number of unknowns, which is due to the redundant force. In order to solve this problem, the relationship between the deformations of the links and the position errors of the moving platform are further explored. Then a novel method, which aims at minimizing the position errors of the machine, is proposed to optimize the redundant force. It also enables to solve the dynamic model. Finally, the dynamic performance analyses of this machine and its non-redundant counterpart are provided by numerical examples. Besides, another optimization method proposed for minimizing the constraint forces is also applied for comparison. The results show the effectiveness of the novel methods in improving the position precision of the machine.

Most existing source search algorithms suffer from a high travel cost, and few of them have been analyzed in performance in noisy environments where local basins are presented. In this paper, the theseus gradient search (TGS) is proposed to effectively overcome local basins in search. Analytical performances of TGS and the gradient ascend with correlated random walk (GACRW), which is a variant of correlated random walk, are derived and compared. A gradient field model is proposed as an analytical tool that makes it feasible to analyze the performances. The analytical average searching costs of GACRW and TGS are obtained for the first time for this class of algorithms in the environments with local basins. The costs, expressed as functions of searching space size, local basin size, and local basin number are confirmed by simulation results. The performances of GACRW, TGS, and two chemotaxis algorithms are compared in the gradient field and a scenario of indoor radio source search in a hallway driven by real data of signal strengths. The results illustrate that GACRW and TGS are robust to noisy gradients and are more competitive than the chemotaxis-based algorithms in real applications. Both analytical and simulation results indicate that in the presence of local basins, TGS almost always costs the lowest.

We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

In this paper, an iterative learning control algorithm is adopted to solve the high-precision trajectory tracking issue of a wheeled mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties. The designed iterative learning control law adopts predictive, current and past learning items to drive the state variables, and input variables, and outputs variables converge to the bounded scope of their desired values. The algorithm can enhance the control performance, stability and robust characteristics. The rigorous mathematical proof of the convergence character of the proposed iterative learning control algorithm is given. The feasibility, effectiveness, and robustness of the proposed algorithm are illustrated by quantitative experiments and comparative analysis. The experimental results show that the proposed iterative learning control algorithm has an outstanding control effect on the trajectory tracking issue of wheeled mobile robots.

There is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits is used to analyze the stability and smoothness performance of the robot. A slide Least Mean Square with adaptive window (SLMS-AW) method is presented for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

The positioning of a wheeled robot is an imperative manipulation problem in mobile robotics. Odometry is a familiar method for determining the relative position of a mobile robot. It comprises the detection of a set of kinematic parameters that permit reconstructing the robot's absolute position and orientation starting from the wheels' encoder measurements. This paper deals with the determination of better relative localization of a mobile robot by means of odometry by considering the influence of parameters namely total weight, speed, diameter of wheel, and width of wheel. Experiments have been conducted based on L9 orthogonal array suggested in Taguchi method to obtain the optimum condition. A mathematical model has also been developed for the mobile robot with the help of MINITAB software.

A disturbance rejection controller is proposed based on the general dynamic model of 3D biped robots. For the first time, with this proposed approach, not only the Zero Moment Point (ZMP) location remains unchanged in presence of disturbances but also the longitudinal and lateral ground reaction forces and the vertical twist moment remain unchanged. This way, slipping as well as tipping is prevented by the controller. The swing phase of the robot's walking gait is considered. An integral sliding mode architecture is chosen for the disturbance rejection. The support forces and moments of the stance foot are the control outputs. The acceleration of the arm/body joints are chosen as the inputs. During the disturbance rejection, the leg joints remain at their desired trajectory. Since the leg joint trajectories are unaffected, the robot is still able to complete its step as planned, even when bounded disturbances are experienced. For simulations, the general method is applied to an 18-degree of freedom biped humanoid robot. Simulations show that the controller successfully mitigates bounded disturbances and maintains all of the support reactions extremely close to their desired values. Consequently, the shift in the position of the ZMP is negligible, and the robot foot does not slip.

In this paper, a rigid–flexible planar parallel manipulator (PPM) actuated by three linear ultrasonic motors for high-accuracy positioning is proposed. Based on the extended Hamilton's principle, a rigid–flexible dynamic model of the proposed PPM is developed utilizing exact boundary conditions. To derive an appropriate low-order dynamic model for the design of the controller, the assumed modes method is employed to discretize elastic motion. Then to investigate the interaction between the rigid and elastic motions, a proportional derivative feedback controller combined with a feed-forward-computed torque controller is developed to achieve motion tracking while attenuating the residual vibration. Then the controller is extended to incorporate an input shaper for the further suppression of residual vibration of flexible linkages. Computer simulations are presented as well as experimental results to verify the proposed dynamic model and controller. The input shaping method is verified to be effective in attenuating residual vibration in a highly coupled rigid–flexible PPM. The procedure employed for dynamic modeling and control analysis provides a valuable contribution into the vibration suppression of such a PPM.

To assist physically disabled people with impaired upper limb function, we have developed a new 7-DOF exoskeleton-type robot named Motion Assistive Robotic-Exoskeleton for Superior Extremity (ETS-MARSE) to ease daily upper limb movements and to provide effective rehabilitation therapy to the superior extremity. The ETS-MARSE comprises a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the lateral side of the upper limb in order to provide naturalistic movements of the shoulder (vertical and horizontal flexion/extension and internal/external rotation), elbow (flexion/extension), forearm (pronation/supination), and wrist joint (radial/ulnar deviation and flexion/extension). This paper focuses on the modeling, design, development, and control of the ETS-MARSE. Experiments were carried out with healthy male human subjects in whom trajectory tracking in the form of passive rehabilitation exercises (i.e., pre-programmed trajectories recommended by a therapist/clinician) were carried out. Experimental results show that the ETS-MARSE can efficiently perform passive rehabilitation therapy.

This paper studies the modeling and analysis of a system with two cooperative manipulators working together on a common task. The task is defined as the transportation of an object in space. The cooperative system is the dual-arm of the humanoid robot Nao, where the serial architecture of each arm has 5 degrees of freedom. The kinematics representing the closed chain system is studied. The mobility of the closed-loop system is analyzed and the nature of the possible motions explored. The stiffness of some motors can be reduced until they behave as passive joints. Certain joints are then chosen as actuated (independent) and the others as passive (dependent). The serial and parallel singular configurations of the system are considered. From the kinematic analysis, admissible and inadmissible minimum actuation schemes are analyzed. Furthermore the dynamic performance of the schemes is compared to find the optimum minimum actuation scheme.

The RepRap 3D printer development project is a fast growing, open-hardware initiative relying on the input of hobbyist designers. One of its key components is the printer nozzle. The performance and reliability deficiencies of currently available nozzle designs are common topics in the RepRap community, and our own experience with a RepRap 3D printer has identified a need for improvement in a few particular areas. We set out to eliminate melt leakage, improve thermal isolation, and develop a more effective method of nozzle assembly attachment. Here, we review the issues, describe design efforts, and report results.

In this paper, an optimal fuzzy sliding mode controller has been designed for controlling the end-effector position in the task space. In the proposed control, feedback linearization method, sliding mode control, first-order fuzzy TSK system and optimization algorithm are utilized. In the proposed controller, a novel heuristic algorithm namely self-adaptive modified bat algorithm (SAMBA) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input membership functions are optimized by SAMBA simultaneously. In this method, the bounds of structural and non-structural uncertainties are reduced by using feedback linearization method, and to overcome the remaining uncertainties, sliding mode control is employed. Mathematical proof demonstrates that the closed loop system with the proposed control has global asymptotic stability. The presence of sliding mode control gives rise to the adverse phenomenon of chattering in the end-effector position tracking in the task space. Subsequently, to prevent the occurrence of chattering in control input, a first-order TSK fuzzy approximator is utilized. Finally, to determine the fuzzy sliding mode controller coefficients, the optimization algorithm of Self-Adaptive Modified Bat is employed. To investigate the performance of the proposed control, a two-degree-of-freedom manipulator is used as a case study. The simulation results indicate the favorable performance of the proposed method.

In the present paper, a model of an underactuated robotic hand with tendon driven fingers is proposed. The aim of the project was to study the feasibility of building a mechanical hand with four or five fingers, the movement of which is achieved using a single linear actuator. The mechanism was first modelled in order to study the possible improvement in the ability of a “robotic hand” powered with a single actuator in regard to grasping objects with complex shapes and also in achieving a strong grip on objects. Next, a model of the finger was studied in order to optimize of its parameters. Finally, a five-fingered robotic hand was modelled for potential application as a human hand prosthesis. Our studies on the dynamic and kinematic behaviour of a single finger mechanism permitted us to make the first prototypes of the mechanism. In addition to modelling studies, we also present a prototype of the modelled robotic hand that was developed in order to optimize functionality and simplicity of construction.

For design using digital human models, human anthropometry data are required as input and are extracted from measurements. There is inherent error associated with these measurements which impacts the output of the simulation. Current techniques in digital human modeling applications primarily employ deterministic methods which are not well suited for handling variability in anthropometric measurement. An alternative to deterministic methods is probabilistic/sensitivity analysis. This study presents a probabilistic sensitivity approach to gain insights into how uncertainty in anthropometric measurements can affect the results of a digital human model with the specific application of vehicle-related reach tasks. Sensitivity levels are found to determine the importance of variability in each joint angle and link length to the final reach. A55-degree of freedom (DOF) digital human model is introduced to demonstrate the sensitivity approach for reach tasks. Seven right-hand reach target points and two left-hand reach target points (creating a total of 14 reach tasks) within a vehicle are used to compare the sensitivities in the joint angles and link lengths resulting from measurement uncertainty. The results show that the importance of each joint angle or link length is dependent on the characteristics of the reach task and sensitivities for joint angles, and link lengths are different for each reach task.

In global localization under the framework of a particle filter, the acquiring of effective observations of the whole particle system will be greatly effected by the uncertainty of a prior-map, such as unspecific structures and noises. In this study, taking the uncertainty of the prior-map into account, a localizability-based action selection mechanism for mobile robots is proposed to accelerate the convergence of global localization. Localizability is defined to evaluate the observations according to the prior-map (probabilistic grid map) and observation (laser range-finder) models based on the Cramér-Rao Bound. The evaluation considers the uncertainty of the prior-map and does not need to extract any specific observation features. Essentially, localizability is the determinant of the inverse covariance matrix for localization. Specifically, at the beginning of every filtering step, the action, which makes the whole particle system to achieve the maximum localizability distinctness, is selected as the actual action. Then there will be the increased opportunities for accelerating the convergence of the particles, especially in the face of the prior-map with uncertainty. Additionally, the computational complexity of the proposed algorithm does not increase significantly, as the localizability is pre-cached off-line. In simulations, the proposed active algorithm is compared with the passive algorithm (i.e. global localization with the random robot actions) in environments with different degrees of uncertainty. In experiments, the effectiveness of the localizability is verified and then the comparative experiments are conducted based on an intelligent wheelchair platform in a real environment. Finally, the experimental results are compared and analyzed among the existing active algorithms. The results demonstrate that the proposed algorithm could accelerate the convergence of global localization and enhance the robustness against the system ambiguities, thereby reducing the failure probability of the convergence.