This paper presents a cable-driven dexterous manipulator with a large, open lumen. One specific application for the manipulator is the treatment of the degeneration of bone tissue (osteolysis) during a less-invasive hip revision surgery. Rigid tools used in traditional approaches limit the surgeons' ability to comprehensively treat the osteolysis due to the complex geometries of the lesion. The surgical scenario, testing, kinematic modeling, and image-based inverse kinematics are described. Testing shows 94% coverage of a lesion wall; the kinematic model describes manipulator notch positions within 0.15 mm, while the image-based inverse kinematics has 0.36 mm error. This manipulator is potentially useful in treating osteolytic lesions through (1) effective lesion exploration compared to conventional techniques, and (2) rapidly performing inverse kinematics from visual feedback.

Several static and dynamic stability criteria have been defined in the course of walking-robot history. Nevertheless, previous work on the classification of stability criteria for statically stable walking machines (having at least four legs) reveals that there is no stability margin that accurately predicts robot stability when inertial and manipulation effects are significant. In such cases, every momentum-based stability margin fails. The use of an unsuitable stability criterion yields unavoidable errors in the control of walking robots. Moreover, inertial and manipulation effects usually appear in the motion of these robots when they are used for services or industrial applications. A new stability margin that accurately measures robot stability considering dynamic effects arising during motion is proposed in this paper. The new stability margin is proven to be the only exact stability margin when robot dynamics and manipulation forces exist. Numerical comparison has been conducted to support the margin's suitability. Stability-level curves are also presented on the basis of a suitable stability margin to control the trajectory of the center of gravity during the support phase.

We address general filtering problems on the Euclidean group SE(3). We first generalize, to stochastic nonlinear systems evolving on SE(3), the particle filter of Liu and West for simultaneous estimation of the state and covariance. The filter is constructed in a coordinate-invariant way, and explicitly takes into account the geometry of SE(3) and P(n), the space of symmetric positive definite matrices. Some basic results for bilinear systems on SE(3) with linear and quadratic measurements are also derived. Three examples—GPS attitude estimation, needle tip location, and vision-based robot end-effector pose estimation—are presented to illustrate the framework.

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

Robotic technology can be used in several ways to benefit people with disabilities. This paper describes the mounting of a robotic arm to a powered wheelchair to assist disabled users in daily activities. Although there are many potential benefits for the disabled user, there are also very strong requirements and compromises, which must be considered in integrating the robotic arm with the wheelchair. This paper focuses on how these integration issues have been addressed.

A computational algorithm is developed to find a dynamic motion trajectory of a mobile manipulator with flexible links and joints that will allow the robot to carry a maximum load between two specified end positions. A compact form of the linearized state space dynamic equations is organized as well as constraint equations. Then, the problem of finding a maximum load carrying capacity on flexible mobile manipulators is formulated as a trajectory optimization problem.

The passivity property of a noncollocated single-link flexible manipulator with a parameterized output is studied. The system can be characterized by either the irrational transfer function of an infinite-dimensional model or its truncated rational transfer functions. Necessary and sufficient conditions for these transfer functions to be passive are found. It is also shown that a non-passive, marginal minimum-phase, truncated transfer function can be rendered passive by using either the root strain feedback or the joint angular acceleration feedback. For the noncollocated truncated passive transfer function, a PD controller suffices to stabilize the overall system. Numerical results are given to show the efficacy of the proposed approaches.

In this paper, we propose a decentralized robust control algorithm for modular and reconfigurable robots (MRRs) based on Lyapunov's stability analysis and backstepping techniques. In using decentralized control schemes with robot manipulators, each joint is considered as an independent subsystem, and the dynamical effects from the other links and joints are treated as disturbance. However, there exist many uncertainties due to unmodeled dynamics, varying payloads, harmonic drive (HD) compliance, HD complex gear meshing mechanisms, etc. Also, while the reconfigurability of MRRs is advantageous, modifying the configuration will result in changes to the robot dynamics parameters, thereby making it challenging to tune the control system. All of the above mentioned disturbances in addition to reconfigurability present a challenge in controlling MRRs. The proposed controller is well suited for MRR applications because of its simple structure that does not require the exact knowledge of the dynamic parameters of the configurations. Desired tracking performance can be achieved via tuning a limited set of parameters of the robust controller. If the numbers of degrees of freedom are held constant, these parameters are shown to be relatively independent of the configuration, and can be held constant between changes in configuration. This strategy is novel compared to existing MRR control methods. In order to validate the controller performance, experimental setup and results are also presented.

In this paper, a Brain–Computer Interface (BCI) control approach for the assistive robotic system FRIEND is presented. The objective of the robot is to assist elderly and persons with disabilities in their daily and professional life activities. FRIEND is presented here from an architectural point of view, that is, as an overall robotic device that includes many subareas of research, such as human–robot interaction, perception, object manipulation and path planning, robotic safety, and so on. The integration of the hardware and software components is described relative to the interconnections between the various elements of FRIEND and the approach used for human–machine interaction. Since the robotic system is intended to be used especially by patients suffering from a high degree of disability (e.g., patients which are quadriplegic, have muscle diseases or serious paralysis due to strokes, or any other diseases with similar consequences for their independence), an alternative non-invasive BCI has been investigated. The FRIEND–BCI paradigm is explained within the overall structure of the robot. The capabilities of the robotic system are demonstrated in three support scenarios, one that deals with Activities of daily living (ADL) and two that are taking place in a rehabilitation workshop. The proposed robot was clinically evaluated through different tests that directly measure task execution time and hardware performance, as well as the acceptance of robot by end-users.

This paper presents an advanced robust active disturbance rejection control (ADRC) for flexible link manipulator (FLM) to track desired trajectories in the joint space and minimize the link’s vibrations. It has been shown that the ADRC technique has a very good disturbance rejection capability. Both the internal dynamics and the external disturbances can be estimated and compensated in real time. The proposed robust ADRC control law is developed to solve the problems existing in the original version of the ADRC related to the disturbance estimation errors and the variation of the parameters. Indeed, these parameters cannot be included in the existing disturbances and then be estimated by the extended state observer. The proposed control law is based on the sliding mode technique, which considers the uncertainties in the control gains and disturbance estimation errors. Lyapunov theory is used to prove the closed-loop stability of the system. The proposed control strategy is simulated and tested experimentally on one FLM. The effect of the observer bandwidth on the system performance is simulated and studied to select the best values of the bandwidth frequency. The simulation and experimental results show that the proposed robust ADRC has better performance than the traditional ADRC.

In this paper a new prosthetic hand is presented that closely approximates the grasping abilities of a human hand. A large variety of different objects can be grasped reliably and the movements of the hand appear to natural. This five-finger hand has 15 degrees of freedom driven by small sized flexible fluidic actuators. The drives are within the fingers allowing a very compact and lightweight hand. Also, a concept for the control of different grasp types is presented. The characteristics of the new hand are illustrated.

This paper concerns the presentation and analysis of a type of three translational degrees of freedom (DoFs) parallel cube-manipulator. The parallel manipulators are the topology architectures of the DELTA robot and Tsai's manipulator, respectively, which have three translational DoFs. In the design, the three actuators are arranged according to the Cartesian coordinate system, which means that the actuating directions are normal to each other, and the joints connecting to the moving platform are located on three sides of a cube, for such reason we call this type of manipulator the parallel cube-manipulator. The kinematics problems, singularity, workspace, compliance characteristic of the manipulator are investigated in the paper. The analysis results show that the manipulators have the advantages of no singularities in the workspace, relatively more simple forward kinematics, and existence of a compliance center. The parallel cube-manipulator can be applied to the fields of micro-motion manipulators, remote center compliance (RCC) devices, assembly, and so on.

The Autonomous Robot Architecture (AuRA) provides multi-level representation and planning capabilities. This paper addresses the task of navigational path-planning, which provides the robot with a path guaranteed to be free of collisions with any modeled obstacles. Knowledge supporting visual perception can also be embedded, facilitating the actual path traversal by the vehicle.

A multi-level representation and architecture to support multi-sensor navigation (predominantly visual) are described. A hybrid vertex-graph free-space representation based upon the decomposition of free space into convex regions capable for use in both indoor and limited outdoor navigation is discussed. This “meadow map” is produced via the recursive decomposition of the initial bounding area of traversability and its associated modeled obstacles. Of particular interest is the ability to handle diverse terrain types (sidewalks, grass, gravel, etc.) “Transition zones” ease the passage of the robot from one terrain type to another.

The navigational planner that utilizes the data available in the above representational scheme is described. An A* search algorithm incorporates appropriate cost functions for multi-terrain navigation. Consideration is given to just what constitutes an “optimal” path in this context.

This paper develops a method for neural network control design with sliding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sliding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved. A switching manifold is prescribed and the phase trajectory is demanded to satisfy both, the reaching condition and the sliding condition for sliding modes.

In this paper, a novel control scheme is proposed to guarantee position and force tracking in nonlinear teleoperation systems subject to varying communication delays. Stability and tracking performance of the teleoperation system are proved using a proposed Lyapunov–Krasovskii functional. To show its effectiveness, the teleoperation controller is simulated on a pair of planar 2-DOF (degree of freedom) robots and experimented on a pair of 3-DOF PHANToM Premium 1.5A robots connected via a communication channel with time-varying delays. Both the planar robots in simulations and the PHANToM robots in experiments possess nonlinear dynamics.

The work presented in this paper aims at providing a unified modelling framework for the reactive control of wheeled mobile manipulators (WMM). Where most work in the literature often provides models, sometimes simplified, of a given type of WMM, an extensive description of obtaining explicit kinematic and dynamic models of those systems is given. This modelling framework is particularly well suited for reactive control approaches, which, in the case of mobile manipulation missions, are often necessary to handle the complexity of the tasks to be fulfilled, the dynamic aspect of the extended workspace and the uncertainties on the knowledge of the environment. A flexible reactive framework is thus also provided, allowing the sequencing of operational tasks (in our case, tasks described in the end-effector frame) whose natures are different but also an on-line switching mechanism between constraints that are to be satisfied using the system redundancy. This framework has been successfully implemented in simulation and on a real robot. Some of the obtained results are presented.

This paper presents theoretical and experimental investigations into the dynamic modelling and characterisation of a flexible manipulator system. A constrained planar single-link flexible manipulator is considered. A dynamic model of the system is developed based on finite element methods. The flexural and rigid dynamics of the system as well as inertia effects and structural damping are accounted in the model. Performance of the algorithm in describing the dynamic behaviour of the system is assessed in comparison to an experimental test-rig. Experimental results are presented for validation of the developed finite element model in the time and frequency domains.

This paper presents a dynamic-level control algorithm to meet simultaneously multiple desired tasks based on allocated priorities for redundant robotic systems. It is shown that this algorithm can be treated as a general framework to achieve control over the whole body of the robot. The control law is an extension of the well-known acceleration-based control to the redundant robots, and considers also possible interactions with the environment occurring at any point of the robot body. The stability of this algorithm is shown and some of the previously developed results are formulated using this approach. To handle the interaction on robot body, null space impedance control is developed within the multi-priority framework. The effectiveness of the proposed approaches is evaluated by means of computer simulation.

The inverse kinematics of a 12 degrees-of-freedom (DOFs) biped robot is formulated in terms of certain parameters. The biped walking gaits are developed using the parameters. The walking gaits are optimized using genetic algorithm (GA). The optimization is carried out considering relative importance of stability margin and walking speed. The stability margin depends on the position of zero-moment-point (ZMP) while walking speed varies with step-size. The ZMP is computed by an approximation-based method which does not require system dynamics. The optimal walking gaits are experimentally realized on a biped robot.

This paper presents a control law for the tracking of a cyclic reference path by an under-actuated biped robot. The robot studied is a five-link planar biped. The degree of under-actuation is one during the single support phase. The control law is defined in such a way that only the geometric evolution of the biped configuration is controlled, but not the temporal evolution. To achieve this objective, we consider a parametrized control. When a joint path is given, a five degree of freedom biped in single support becomes similar to a one degree of freedom inverted pendulum. The temporal evolution during the geometric tracking is completely defined and can be analyzed through the study of a model with one degree of freedom. Simple analytical conditions, which guarantee the existence of a cyclic motion and the convergence towards this motion, are deduced. These conditions are defined on the reference trajectory path. The analytical considerations are illustrated with some simulation results.