This paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

There have been significant interests and efforts in the field of impedance control on robotic manipulation over last decades. Impedance control aims to achieve the desired mechanical interaction between the robotic equipment and its environment. This paper gives the overview and comparison of basic concepts and principles, implementation strategies, crucial techniques, and practical applications concerning the impedance control of robotic manipulation. This work attempts to serve as a tutorial to people outside the field and to promote discussion of a unified vision of impedance control within the field of robotic manipulation. The goal is to help readers quickly get into the problems of their interests related to impedance control of robotic manipulation and to provide guidance and insights in finding appropriate strategies and solutions.

Kinematic calibration plays an important role in the improvement of positioning accuracy for parallel manipulators. Based on the specific geometric constraints of limbs, this paper presents a new kinematic parameter identification method for the widely studied 3-PRS parallel manipulator. In the proposed calibration method, the planes where the PRS limbs exactly located are identified firstly as the geometric characteristics of the studied parallel manipulator. Then, the limbs can be considered as planar PR mechanisms whose kinematic parameters can be determined conveniently according to the limb planes identified in the first step. The main merit of the proposed calibration method is that the system error model which relates the manipulator’s kinematic errors to the output ones is not required for kinematic parameter identification. Instead, only two simple geometric problems need to be established for identification, which can be solved readily using gradient-based searching algorithms. Hence, another advantage of the proposed method is that parameter identification of the manipulator’s limbs can be accomplished individually without interactive impact on each other. In order to validate the effectiveness and efficiency of the proposed method, calibration experiments are conducted on an apparatus of the studied 3-PRS parallel manipulator. The results show that using the proposed two-step calibration method, the kinematic parameters can be identified quickly by means of gradient searching algorithm (converge within five iterations for both steps). The positioning accuracy of the studied 3-PRS parallel manipulator has been significantly improved by compensation according to the identified parameters. The mean position and orientation errors at the validation configurations have been reduced to 1.56 × 10−4 m and 1.13 × 10−3 rad, respectively. Further, the proposed two-step kinematic calibration method can be extended to other limited-degree-of-freedom parallel manipulators, if proper geometric constraints can be characterized for their kinematic limbs.

Designers of robotic groups are faced with the formidable task of creating effective coordination architectures that can deal with collisions due to changing environment conditions and hardware failures. Communication between robots is a mechanism that can at times be helpful in such systems, but can also create a time, energy, or computation overhead that reduces performance. In dealing with this issue, different communication schemes have been proposed ranging from those without any explicit communication, localized algorithms, and centralized or global communicative methods. Finding the optimal communication act is typically an intractable problem in real-world problems. As a result, we argue that at times group designers should use computationally bounded team communication approaches. We propose two such approaches: an algorithm selection approach to communication whereby robots choose between a known group of communication schemes and a parameterized communication framework whereby robots can reason about how large a communication radius is needed for a given problem. Both solutions use a novel coordination cost measure, combined coordination costs, to find the appropriate level of communication within such groups. Robots can then use this measure to create adaptive communication approaches that select between communication approaches as needed during task execution. We validated this approach through conducting extensive experiments in a canonical robotic foraging domain and found that robotic groups using these adaptive methods were able to significantly increase their productivity compared to teams that used only one type of communication scheme.

Recently, the idea of applying “jamming” of appropriate media has been exploited for a novel continuum robot design. It is completed by applying vacuum in a robot structure filled with granular media. The backbone deformation and motion are achieved by controlling the fluid pressure. A jammable robotic manipulator does not certainly follow constant curvature during bending, that is, gravitational loads cause section sag. The kinematics describes the deformation of continuum manipulators. This formulation is expected to facilitate additional synthesis and analysis on workspace. This paper presents a Jacobian-based approach to obtain the forward kinematics solution. The proposed kinematic formulation in this paper tries to combine the key advantages of the techniques in constant curvature and variable curvature models. Hence, the deformation of any arbitrary bending is modeled. The workspace synthesis is continued by kinematic analysis, and in this regard, the manipulability measure is computed. This is an important improvement when compared with existing work for this kind of manipulators. It shows how manipulability measure can determine the workspace quality, where usually reachability is used for robot’s capabilities representation. As a result, the forward kinematics and manipulability analysis based on a piecewise-constant-curvature approximation are discussed in the simulation. The simulation has been carried out according to the fabricated experimental robot.

This paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in the SE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.

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.

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.

This paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.