In this paper, new hybrid control laws for the position control of robotic manipulators are proposed. The proposed control laws are composed of discrete feedforward component and continuous feedback component. The open loop nominal torque about the desired trajectories is taken as the feedforward component, while a modified version of the sliding mode control is taken as the feedback component. For the three proposed control laws, we give sufficient conditions which guarantee the bounded tracking errors in spite of the modeling errors. The existence of the control gains which satisfy these conditions is shown by numerical examples. The computational burden of feedback control is analyzed, which shows that the feedback control can be used in real time digital control. The robustness and the good tracking performance of proposed algorithms are demonstrated by the numerical simulation of a manipulator position control under payloads and parameter uncertainties.

The goal of this research is to establish a methodology to actively control a pneumatically driven robotic device that can induce specific muscle force patterns in target muscles during a subject's voluntary movement. In this paper, the generation of constant forces in the rectus femoris muscle throughout the knee extension, i.e., isotonic contractions, was studied. Due to a highly nonlinear nature of mapping the joint torque to muscle force, a simple application of constant torques to the knee joint would not realize isotonic contractions. The proposed robotic exercise accounted for nonlinear moment arms of muscles as functions of joint angles and nonlinear coordination of multiple muscles in the neuromuscular system to accomplish individual muscle control. A pneumatically powered one degree-of-freedom device that can impose active force feedback control has been designed and built. An exercise-planning algorithm has been developed that involved a musculoskeletal model of the lower body, and the dynamics of a pneumatic actuator. Five constant force profiles were tested for 20 healthy volunteers and electromyographic signals were collected while the device was applying calculated force profiles.

This paper presents a virtual headstick system as an alternative to the conventional passive headstick for persons with limited upper extremity function. The system is composed of a pair of kinematically dissimilar master-slave robots with the master robot being operated by the user's head. At the remote site, the end-effector of the slave robot moves as if it were at the tip of an imaginary headstick attached to the user's head. A unique feature of this system is that through force-reflection, the virtual headstick provides the user with proprioceptive information as in a conventional headstick, but with an augmentation of workspace volume and additional mechanical power. This paper describes the test-bed development, system identification, bilateral control implementation, and system performance evaluation.

An efficient algorithm for generating an optimal plan for part-bringing tasks, using robotic manipulators, is introduced. The task of transporting a micro-part in a partially unstructured environment, that includes obstacles whose locations are not initially known, is introduced with the optimal plan formulated on the basis of the observed environmental conditions. Fuzzy set theory, well-suited to the management of uncertainty, is introduced to address the uncertainty associated with the part-bringing procedure. A part-bringing algorithm for generating the optimal plan related to a part assembly, despite existing obstacles, is presented. It is shown that the machine organizer using a sensor system can intelligently determine an optimal plan, based on explicit performance criteria, to overcome environmental uncertainty. The algorithm utilizes knowledge processing functions such as machine reasoning, planning, memory, and decision-making. Simulation results show the effectiveness of the proposed approach. The proposed algorithm is applicable not only to a wide range of robotic tasks including pick and place operations and maneuvering mobile based robots around obstacles, but also to the control of unmanned aircraft.

This paper proposes a shared control scheme which aims to achieve a stable control of the speed and turn of a bipedal robot during a delayed bilateral teleoperation. The strategy allows to get a delay-dependent damping value that must be injected to assure a bounded response of the hybrid system, while simultaneously, the human operator receives a force feedback that help him to decrease the synchronism error. Furthermore, a test where a human operator handles the walking of a simulated bipedal robot, to follow a curve path in front of varying time delay, is performed and analyzed.

A method was developed that takes into account flexibility of robot links in the inverse dynamics calculations. This method uses the Newton-Euler equations and is applicable for special case systems that allow for only a small degree of flexibility. Application of the method should improve the accuracy of the position of the end effector during motion of the robot.

The results of this study show that the method can be based entirely on an existing rigid-link model with only minimal changes required as additions. The computational complexity of the method is discussed briefly as well and indicates an increase of computations of slightly more than a factor of two as compared to a rigid-link model for the same robot geometry.

In this paper, the 3D self-positioning problem of a mobile robot is investigated under the assumption that there are given a set of guide points along with camera vision as the detection mechanism. The minimal number of guide points is discussed to determine the position and orientation of a mobile robot via a single or multiple camera system. For practical application, a closed form 3D self-positioning algorithm is proposed using a stereo camera system with triple guide points. It is further shown that a double triangular pattern is an effective guide-mark that is robust against measurement noise in feature extraction. Then, by simulation, the sensitivity of positioning errors due to image errors are analyzed. It is experimentally shown that the proposed method with triple guide points works well for a walking robot equipped with a stereo camera in laboratory environment.

Many motion planning methods use Configuration Space to represent a robot manipulator's range of motion and the obstacles which exist in its environment. The Cartesian to Configuration Space mapping is computationally intensive and this paper describes how the execution time can be decreased by using parallel processing. The natural tree structure of the algorithm is exploited to partition the computation into parallel tasks. An implementation programmed in the occam2 parallel computer language running on a network of INMOS transputers is described. The benefits of dynamically scheduling the tasks onto the processors are explained and verified by means of measured execution times on various processor network topologies. It is concluded that excellent speed-up and efficiency can be achieved provided that proper account is taken of the variable task lengths in the computation.

This paper presents an example of the application of geometric and algebraic approaches to the inverse kinematics problem of four-link robot manipulators. A special arm configuration of the robot manipulator is employed for solving the inverse kinematics problem by using the geometric approach. The obtained joint variables as angular positions are defined in the form of cubic polynomials. The other kinematic parameters of the joints, such as angular velocities and angular accelerations, are the time derivatives of these polynomials. It is evident that there is no definite difference between the results of the two approaches. Consequently, if an appropriate arm configuration for the geometric approach can be established, the inverse kinematics can be solved in a simpler and shorter way.

This paper outlines a method for control of robotic systems for part sorting operations in unstructured manufacturing environments. The sorting is accomplished simply and without any change in the kinematic structure of the robot or any need for inclusion of complicated external sensory systems. The method developed is based on the inherent relationship between the mass of parts to be carried and the maximum velocity of the tool centre point attainable along certain specified paths.

The actuators' maximum velocity is detected and is communicated to a devised expert system. The expert system uses this maximum velocity as a part of its matching procedure for the purpose of part sorting.

For greater autonomy of visual control-based solutions, especially applied to mobile robots, it is necessary to consider the existence of unevenness in the navigation surface, an intrinsic characteristic of several real applications. In general, depth information is essential for navigating three-dimensional environments and for the consistent parameter calibration of the visual models. This work proposes a new solution, including depth information in the visual path-following (VPF) problem, which allows the variation of the perception horizon at runtime while forcing the coupling between optical and geometric quantities. A new NMPC (nonlinear model predictive control) framework considering the addition of a new input to an original solution for the constrained VPF-NMPC allows the maintenance of low computational complexity. Experimental results in an outdoor environment with a medium-sized commercial robot demonstrate the correctness of the proposal.

An efficient obstacle detection technique is required so that navigating robots can avoid obstacles and potential hazards. This task is usually simplified by relying on structural patterns. However, obstacle detection constitutes a challenging problem in unstructured unknown environments, where such patterns may not exist. Talukder et al. (2002, IEEE Intelligent Vehicles Symposium, pp. 610–618.) successfully derived a method to deal with such environments. Nevertheless, the method has a high computational cost and researchers that employ it usually rely on approximations to achieve real-time. We hypothesize that by using a graphics processing unit (GPU), the computing time of the method can be significantly reduced. Throughout the implementation process, we developed a general framework for processing dynamically-sized sliding windows on a GPU. The framework can be applied to other problems that require similar computation. Experiments were performed with a stereo camera and an RGB-D sensor, where the GPU implementations were compared to multi-core and single-core CPU implementations. The results show a significant gain in the computational performance, i.e. in a particular instance, a GPU implementation is almost 90 times faster than a single-core one.

Autonomous operation of mechanical systems often requires the ability to detect and locate a particular phenomenon occurring in the surrounding environment. Being implemented to articulated manipulation, such a capability may realize a wide range of applications in autonomous maintenance and repair. This paper presents the sensor-driven task space control of an end-effector that combines the field estimation and the target tracking in an unknown spatial field of interest. The radial basis function network is adopted to model spatial distribution of an environmental phenomenon as a scalar field. Their weight parameters are estimated by a recursive least square method using collective measurements from the on-board sensors mounted to the manipulator. Then the asymptotic source tracking has been achieved by the control law based on the gradient of the estimated field. A new singularity tolerant scheme has been suggested to command the task space control law despite singular configurations. Simulation results using the three-link planar robot and the 6-revolute elbow manipulator are presented to validate the main ideas.

The design, control, and actuation of legged robots that walk is well established, but there remain unsolved problems for legged robots that run. In this work, dynamic principles are used to develop a set of heuristics for controlling bipedal running and acceleration. These heuristics are then converted into control laws for two very different bipedal systems: one with a high-inertia torso and prismatic knees and one with a low-inertia torso, articulated knees, and mechanical coupling between the knee and ankle joints. These control laws are implemented in simulation to achieve stable steady-state running, accelerating, and decelerating. Stable steady-state running is also achieved in a planar experimental system with a semiconstrained torso.

Since a slave manipulator with a high reduction ratio joint generally has slow dynamics in comparison with a master manipulator in telemanipulation systems, its control input is likely to be saturated resulting in poor position tracking performance and deteriorated stability. This paper proposes a force reflecting control scheme for telemanipulators with a high reduction ratio joint, which can effectively compensate the control input saturation caused by the high ratio gear reducer at its joint. The proposed scheme is also shown to guarantee the stability and provides an excellent position tracking performance regardless of the saturation.

Path planning has been widely investigated by many researchers and engineers for its extensive applications in the real world. In this paper, a biharmonic radial basis potential function (BRBPF) representation is proposed to construct navigation fields in 2D maps with obstacles, and it therefore can guide and design a path joining given start and goal positions with obstacle avoidance. We construct BRBPF by solving a biharmonic equation associated with distance-related boundary conditions using radial basis functions (RBFs). In this way, invalid gradients calculated by finite difference methods in large size grids can be preventable. Furthermore, paths constructed by BRBPF are smoother than paths constructed by harmonic potential functions and other methods, and plenty of experimental results demonstrate that the proposed method is valid and effective.

This paper studies the synchronization control of the blanket remote maintenance robot (BRMR) of the China fusion engineering test reactor (CFETR). First, the general state space mathematical model of BRMR was established by using a physical-based method. Second, based on the receding horizon optimization of model predictive control (MPC) and cross-coupling error reduction in cross-coupling control (CCC), the innovative MPC-CCC controller was proposed to realize the single-system and multisystem error convergence and high accuracy transportation of blanket through the high accuracy synchronization control of BRMR. Third, to verify the control effectiveness of the MPC-CCC controller, two types of simulations and experiments were implied compared with the original proportional-integral (PI) controller in Mover. Results showed that simulation and experiments were highly consistent. It is found that the use of an MPC-CCC controller can result in up to a 70% reduction in displacement error and up to a 59% reduction in synchronization error compared to the PI controller. And the accuracy of the MPC-CCC controller satisfies the real requirement of the maintenance process of the blanket. This work provides the theoretical basis and practical experience for the highly stable, safe, and efficient maintenance of blankets in the future.