This study presents an optimal design procedure including topology optimization and size–shape optimization methods to maximize mechanical advantage (which is defined as the ratio of output force to input force) of the synthesized compliant mechanism. The formulation of the topology optimization method to design compliant mechanisms with multiple output ports is presented. The topology-optimized result is used as the initial design domain for subsequent size–shape optimization process. The proposed optimal design procedure is used to synthesize an adaptive compliant gripper with high mechanical advantage. The proposed gripper is a monolithic two-finger design and is prototyped using silicon rubber. Experimental studies including mechanical advantage test, object grasping test, and payload test are carried out to evaluate the design. The results show that the proposed adaptive complaint gripper assembly can effectively grasp irregular objects up to 2.7 kg.

This document proposes a control scheme for delayed bilateral teleoperation of a mobile robot, which it is sought to achieve a coordination of the master device position with the slave mobile robot velocity, and at the same time synchronize the force exerted by the operator with force applied by the environment over the mobile robot. This approach allows the operator to improve the sensitive perception of the remote environment in which the robot navigates while he generates commands to control the mobile robot motion. In this paper, variable and asymmetrical communication time delays are taken into account, as well as a non-passive model of the human operator, for which a novel model is proposed that has a more general structure than the typical ones used to date in the teleoperation field. Furthermore, based on the theoretical analysis presented, the state of convergence in the stationary response is obtained. In addition, an experimental performance evaluation is carried out, where the position–velocity error, force error and the time to complete the task are evaluated. In the tests, a human operator commands a remote mobile robot to push objects of different weight while he perceives the weight of each object through the force feedback system. As an outcome, the theoretical and practical results obtained allow concluding that a satisfactory trade-off between stability and transparency is reached.

In this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed.

The potential use of onboard vision sensors (e.g., cameras) has long been recognized for the Sense and Avoid (SAA) of unmanned aerial vehicles (UAVs), especially for micro UAVs with limited payload capacity. However, vision-based SAA for UAVs is extremely challenging because vision sensors usually have limitations on accurate distance information measuring. In this paper, we propose a monocular vision-based UAV SAA approach. Within the approach, the host UAV can accurately and efficiently avoid a noncooperative intruder only through angle measurements and perform maneuvers for optimal tradeoff among target motion estimation, intruder avoidance, and trajectory tracking. We realize this feature by explicitly integrating a target tracking filter into a nonlinear model predictive controller. The effectiveness of the proposed approach is verified through extensive simulations.

This paper deals with the kinematic analysis and enumeration of singularities of the six degree-of-freedom 3-RPS-3-SPR series–parallel manipulator (S–PM). The characteristic tetrahedron of the S–PM is established, whose degeneracy is bijectively mapped to the serial singularities of the S–PM. Study parametrization is used to determine six independent parameters that characterize the S–PM and the direct kinematics problem is solved by mapping the transformation matrix between the base and the end-effector to a point in ℙ7. The inverse kinematics problem of the 3-RPS-3-SPR S–PM amounts to find the location of three points on three lines. This problem leads to a minimal octic univariate polynomial with four quadratic factors.

Parallel manipulators, especially those with outputs as one translation and two rotations (1T2R), are being increasingly studied. The kinematic chains of parallel manipulators share the loads and make the stiffness higher than the stiffness of serial manipulators with equivalent limbs. This high stiffness ensures a minimal deformation of the limbs, allowing a high positioning accuracy of the endeffector. Thus, it is very important to be able to measure the stiffness in parallel manipulators. In this work, we present a novel 1T2R multi-axial shaking table (MAST) for automobile pieces testing purposes—the 2PRU–1PRS parallel manipulator—and focus on the analysis of its stiffness all over the useful workspace. Analysis methods based on matrix structural method need to be validated for every parallel manipulator, and we present these steps along with a comparison between experimental and analytical methods.

This paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.

Designing the boundary layer thickness and switching gain in the nonlinear part of sliding mode controller (SMC) is one of the main subjects in SMC design that needs human experience, knowledge on the amplitude of disturbances, and information about the bounds of system uncertainties. In this paper, to reduce the trial-and-error effort by the designer(s) two different fitness functions in the horizontal and vertical planes are presented and a heuristic method is used for their optimization. The optimal switching gain in the proposed approach properly compensates the unmodeled dynamics, model uncertainty, and external disturbances. Chattering phenomenon in control signals and noise measurement effect are reduced by the optimal selection of boundary layer thickness. This proposed method is applied to an autonomous underwater vehicle (AUV) and evaluated through the real-time and cost-effective manner. The execution code is implemented on a single-board computer (SBC) through the xPC Target and is evaluated by the processor-in-the-loop (PIL) test. The results of the PIL test in the two different test cases indicate that the chattering phenomenon and amplitude of control signal applied to the actuators are reduced in comparison with the three conventional approaches in the AUV motion control.

This study investigates the effectiveness of a robot-mediated strategy aimed at promoting balance recovery after multidirectional slippages. Six older adults were asked to manage anteroposterior and mediolateral slippages while donning an active pelvis orthosis (APO). The APO was set up either to assist volunteers during balance loss or to be transparent. The margin of stability, in sagittal and frontal planes, was the main metric to assess the effectiveness of balance recovery. Results showed that the assistive strategy is effective at promoting balance recovery in the sagittal plane, for both perturbing paradigms; however, it is not effective at controlling stability in the frontal plane.

Due to physically demanding tasks in construction, workers are exposed to significant safety and health risks. Measuring and evaluating body kinematics while performing tasks helps to identify the fundamental causes of excessive physical demands, enabling practitioners to implement appropriate interventions to reduce them. Recently, non-invasive or minimally invasive motion capture approaches such as vision-based motion capture systems and angular measurement sensors have emerged, which can be used for in-field kinematics measurements, minimally interfering with on-going work. Given that these approaches have pros and cons for kinematic measurement due to adopted sensors and algorithms, an in-depth understanding of the performance of each approach will support better decisions for their adoption in construction. With this background, the authors evaluate the performance of vision-based (RGB-D sensor-, stereovision camera-, and multiple camera-based) and an angular measurement sensor-based (i.e., an optical encoder) approach to measure body angles through experimental testing. Specifically, measured body angles from these approaches were compared with the ones obtained from a marker-based motion capture system that has less than 0.1 mm of errors. The results showed that vision-based approaches have about 5–10 degrees of error in body angles, while an angular measurement sensor-based approach measured body angles with about 3 degrees of error during diverse tasks. The results indicate that, in general, these approaches can be applicable for diverse ergonomic methods to identify potential safety and health risks, such as rough postural assessment, time and motion study or trajectory analysis where some errors in motion data would not significantly sacrifice their reliability. Combined with relatively accurate angular measurement sensors, vision-based motion capture approaches also have great potential to enable us to perform in-depth physical demand analysis such as biomechanical analysis that requires full-body motion data, even though further improvement of accuracy is necessary. Additionally, understanding of body kinematics of workers would enable ergonomic mechanical design for automated machines and assistive robots that helps to reduce physical demands while supporting workers' capabilities.

The paper introduces the use of radial basis functions (RBFs) to generate smooth point-to-point joint trajectories for robot manipulators. First, Gaussian RBF interpolation is introduced taking into account boundary conditions. Then, the proposed approach is compared with classical planning techniques based on polynomial and trigonometric models. Also, the trajectory planning problem involving via-points is solved using the proposed RBF interpolation technique. The obtained trajectories are then compared with those synthesized using algebraic and trigonometric splines. Finally, the proposed method is tested for the six-joint PUMA 560 robot in two cases (minimum time and minimum time-jerk) and results are compared with those of other planning techniques. Numerical results demonstrate the advantage of the proposed technique, offering an effective solution to generate trajectories with short execution time and smooth profile.

This paper presents a robust tracking controller for electrically driven robots, without the need for velocity measurements of joint variables. Many observers require the system dynamics or nominal models, while a model-free observer is presented in this paper. The novelty of this paper is presenting a new observer–controller structure based on function approximation techniques and Stone–Weierstrass theorem using differential equations. In fact, it is assumed that the lumped uncertainty can be modeled by linear differential equations. Then, using Stone–Weierstrass theorem, it is verified that these differential equations are universal approximators. The advantage of proposed approach in comparison with previous related works is simplicity and reducing the dimensions of regressor matrices without the need for any information of the systems’ dynamic. Simulation results on a 6-degrees of freedom robot manipulator driven by geared permanent magnet DC motors indicate the satisfactory performance of the proposed method in overcoming uncertainties and reducing the tracking error. To evaluate the performance of proposed controller in practical implementations, experimental results on an SCARA manipulator are presented.

This paper presents a new approach to synthesize multi-loop mechanisms with three translational displacement parameters based on virtual-loop theory and Assur groups. The approach used kinematic links as a generalized link group added one-by-one to the output link, which further extends the unified link groups in the plane and space. Firstly, the concept of infinitesimal displacement parameters is introduced to describe the displacement parameters. The dependence on the change in the degree of freedom (DOF) and displacement parameters of the output link after adding a 0-DOF generalized link group is established. Then, the link groups with three displacement parameters are synthesized, and the intersection operation rules are given. The single-loop mechanism is synthesized under two circumstances. The 1-, 2-, and 3-DOF dual-loop mechanisms are obtained by adding corresponding generalized link groups. Finally, the multi-loop mechanisms are obtained by adding corresponding generalized link groups. Some novel mechanisms are synthesized to illustrate the effectiveness of the proposed approach.

This paper presents a novel approach to implement bilateral control loops between local haptic devices and remote industrial manipulators using a layer of simulation and virtual reality. The remote scene of manipulation has been visualized in an open-source software environment, where forward and inverse kinematics of the manipulators can be computed. Therefore, the explicit knowledge of mathematical models of the robots is not required for the implementation of the proposed bilateral control schemes. A haptic coupling has been designed between the human operator and the task in the remote environment. Virtually introduced force feedback has contributed to the performance of the proposed bilateral loop by facilitating the adaptation of unexperienced human operators. Teleoperation of one remote manipulator has been experimentally demonstrated with the proposed controllers. Structural modularity of the bilateral haptic control schemes makes them directly extendable for the teleoperation of multiple collaborative robots. Stability and transparency of the proposed bilateral haptic controllers have been theoretically and experimentally investigated.

Robots often need to accomplish some complex tasks such as surveillance, response and obstacle avoidance. In this paper, a dynamic search method is proposed to generate optimal robot trajectories satisfying complex task requirement in uncertain environment. The LTL-A* algorithm is presented to generate a global optimal path and the A* algorithm is provided to modify the global optimal path. The task is specified by a linear temporal logic (LTL) formula, and a weighted transition system according to the known information in uncertain environment is modeled to describe the robot motion. Subsequently, a product automaton is constructed by combining the transition system with the task requirement. Based on the product automaton, the LTL-A* algorithm is proposed to generate a global optimal path. The local path planning based on the A* algorithm is employed to deal with the environment change during the process of tracking the global optimal path for the robot. The results of the simulation and experiments show that the proposed method can not only meet the complex task requirement in uncertain environment but also improve the search efficiency.

Saturation nonlinearities, among the known challenges in control engineering, are ubiquitous in robotic systems and can lead to stability and performance degradation. In this paper, an adaptive dynamic surface impedance (ADSI) control approach is developed for an n-link robotic manipulator by employing self-recurrent wavelet neural networks (SRWNNs) in order to overcome the saturation effect. The proposed control approach is inspired by the theory of dynamic surface control (DSC) and SRWNNs. As a novel application of the dynamic surface method to obtain a simple structure, the target impedance is formulated in the state–space, and effective dynamic surfaces are defined to track the desired impedance behavior. In fact, DSC is used to force the robot manipulator to track the desired impedance, while the robot interacts with an environment. In addition, SRWNNs are applied to approximate the parametric uncertainties and external disturbances in the robot dynamical model. Self-feedback neurons are embedded as memory units in SRWNNs to model the sudden dynamic jumps of the environment. Using Lyapunov's method, an ADSI controller is designed, and adaptation laws are induced to guarantee the stability of the closed-loop system. Finally, simulations are conducted to verify the proper performance of the proposed approach for removing the saturation effect and tracking the target impedance. It is worth noting that the simulation results indicate the robustness of the controller against uncertainties and external disturbances.

PMs with two rotations and one translation (2R1T) have been used as skeletons in various advanced manufacturing equipment where high accuracy and stiffness are basic requirements. Considering the advantages of redundant actuation and overconstrained structure, such as reduced singularities and improved stiffness, a new 2R1T overconstrained PM with actuation redundancy, called Hex4, is proposed in this paper. This is a 2-PUR/2-RPU PM (where P denotes an actuated prismatic joint, U a universal joint, and R a revolute joint) that is actuated by four prismatic joints. Compared with some existing 2R1T overconstrained PMs with actuation redundancy, the main advantage of the proposed PM is that the heavy motors of two limbs are mounted on the base to reduce the movable mass and improve dynamic response. First, mobility analysis, inverse kinematics, and velocity analysis are presented. Then, the local transmission index and good transmission workspace are used to evaluate the motion/force transmissibility of the Hex4 PM. The variation tendencies of the two indices with different link parameters are investigated. The singularity is then discussed by considering the motion/force transmissibility. Finally, link parameters are optimized to obtain an improved good transmission workspace. It is shown that the proposed PM has a good potential for high precision applications.

In this study, the kinematics and dynamics of a single actuator wave (SAW)-like robot are explored. Comprising a helical spine and links, SAW has the potential for miniaturization. A kinematic model for SAW is firstly established, and the dynamic equation of motion is derived based on Kane’s method. For validation, the motion of SAW is simulated using both MATLAB and ADAMS, and the comparison of results demonstrates the effectiveness of the theoretical models. Then the inverse dynamic analysis is performed to reveal the power consumption. Finally, robot prototypes are developed and tested to confirm the robot velocity predicted by simulations.

People with severe neuromuscular trunk impairment cannot maintain or control upright posture of the upper body in sitting while reaching. Passive orthoses are clinically available to provide support and promote the use of upper extremities in this population. However, these orthoses only position the torso passively without any degree of trunk movement.

We introduce for the first time a novel active-assistive torso brace called Wheelchair Robot for Active Postural Support (WRAPS). It consists of two rings over the hips and chest connected by a 2RPS-2UPS parallel robotic device. WRAPS can modulate the displacement of the upper ring and/or the forces applied on the torso through the ring in four degrees-of-freedom (DOF), including rotations and translation in the sagittal and frontal planes.

In the present study, we evaluate the design of WRAPS and its functions. Moreover, we discuss the potential effectiveness of WRAPS as a therapeutic robotic tool in people with severe trunk control deficits. The performance of WRAPS was evaluated in seated healthy subjects. Kinematics and surface electromyography (sEMG) were collected when the participants performed selective trunk movements. First, the torso range of motion (tROM) was calculated with WRAPS in transparent mode—zero-force control mode—which was compared with free-guided tROM (no WRAPS) with motion capture system. Second, a position control mode was configured to mobilize the torso along the trajectories obtained with the transparent mode.

Our results show that the design of WRAPS suited well the subject’s anthropometrics while supporting the weight of the torso. Importantly, WRAPS can be programmed to replicate the subject’s tROM, without the full activation of torso muscles. This can be critical in individuals with no trunk control. Altogether, these preliminary results indicate the potential applicability of WRAPS to promote active-assistive trunk mobility in people who cannot sit independently because of trunk dysfunction.

In this paper, we present a new leader–follower type solution to the translational maneuvering problem for formations of multiple, non-holonomic wheeled mobile robots. The solution is based on the graph that models the coordination among the robots being a spanning tree. Our control law incorporates two types of position errors: individual tracking errors and coordination errors for leader–follower pairs in the spanning tree. The control ensures that the robots globally acquire a given planar formation while the formation as a whole globally tracks a desired trajectory, both with uniformly ultimately bounded errors. The control law is first designed at the kinematic level and then extended to the dynamic level. In the latter, we consider that parametric uncertainty exists in the equations of motion. These uncertainties are accounted for by employing an adaptive control scheme. The main contributions of this work are that the proposed control scheme minimizes the number of control links and global position measurements, and accounts for the uncertain vehicle dynamics. The proposed formation maneuvering controls are demonstrated experimentally and numerically.