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Surgical robotics can be divided into two groups: specialized and versatile systems. Versatile systems can be used in different surgical applications, control architectures and operating room set‐ups, but often still based on the adaptation of industrial robots. Space consumption, safety and adequacy of industrial robots in the unstructured and crowded environment of an operating room and in close human robot interaction are at least questionable. The purpose of this paper is to describe the DLR MIRO, a new versatile lightweight robot for surgical applications.

The design approach of the DLR MIRO robot focuses on compact, slim and lightweight design to assist the surgeon directly at the operating table without interference. Significantly reduced accelerated masses (total weight 10 kg) enhance the safety of the system during close interaction with patient and user. Additionally, MIRO integrates torque‐sensing capabilities to enable close interaction with human beings in unstructured environments.

A payload of 30 N, optimized kinematics and workspace for surgery enable a broad range of possible applications. Offering position, torque and impedance control on Cartesian and joint level, the robot can be integrated easily into telepresence (e.g. endoscopic surgery), autonomous or soft robotics applications, with one or multiple arms.

This paper considers lightweight and compact design as important design issues in robotic assistance systems for surgery.

The paper seeks to present a new generation of torque‐controlled light‐weight robots (LWR) developed at the Institute of Robotics and Mechatronics of the German Aerospace Center.

An integrated mechatronic design approach for LWR is presented. Owing to the partially unknown properties of the environment, robustness of planning and control with respect to environmental variations is crucial. Robustness is achieved in this context through sensor redundancy and passivity‐based control. In the DLR root concept, joint torque sensing plays a central role.

In order to act in unstructured environments and interact with humans, the robots have design features and control/software functionalities which distinguish them from classical robots, such as: load‐to‐weight ratio of 1:1, torque sensing in the joints, active vibration damping, sensitive collision detection, compliant control on joint and Cartesian level.

The DLR robots are excellent research platforms for experimentation of advanced robotics algorithms. Space and medical robotics are further areas for which these robots were designed and hopefully will be applied within the next years. Potential industrial application fields are the fast automatic assembly as well as manufacturing activities done in cooperation with humans (industrial robot assistant). The described functionalities are of course highly relevant also for the potentially huge market of service robotics. The LWR technology was transferred to KUKA Roboter GmbH, which will bring the first arms on the market in the near future.

This paper introduces a new type of LWR with torque sensing in each joint and describes a consistent approach for using these sensors for manipulation in human environments. To the best of one's knowledge, the first systematic experimental evaluation of possible injuries during robot‐human crashes using standardized testing facilities is presented.

Stiffness control of redundant robot arm, aimed at using extra degrees of freedom (DoF) to shape the robot tool center point (TCP) elastomechanical behavior to be consistent with the essential requirements needed for a successful part mating process, i.e., to mimic part supporting mechanism with selective quasi-isotropic compliance (Remote Center of Compliance – RCC), with additional properties of inherent flexibility.

Theoretical analysis and synthesis of the complementary projector for null-space stiffness control of kinematically redundant robot arm. Practical feasibility of the proposed approach was proven by extensive computer simulations and physical experiments, based on commercially available 7 DoF SIA 10 F Yaskawa articulated robot arm, equipped with the open-architecture control system, system for generating excitation force, dedicated sensory system for displacement measurement and a system for real-time acquisition of sensory data.

Simulation experiments demonstrated convergence and stability of the proposed complementary projector. Physical experiments demonstrated that the proposed complementary projector can be implemented on the commercially available anthropomorphic redundant arm upgraded with open-architecture control system and that this projector has the capacity to efficiently affect the task-space TCP stiffness of the robot arm, with a satisfactory degree of consistency with the behavior obtained in the simulation experiments.

A novel complementary projector was synthesized based on the adopted objective function. Practical verification was conducted using computer simulations and physical experiments. For the needs of physical experiments, an adequate open-architecture control system was developed and upgraded through the implementation of the proposed complementary projector and an adequate system for generating excitation and measuring displacement of the robot TCP. Experiments demonstrated that the proposed complementary projector for null-space stiffness control is capable of producing the task-space TCP stiffness, which can satisfy the essential requirements needed for a successful part-mating process, thus allowing the redundant robot arm to mimic the RCC supporting mechanism behavior in a programmable manner.

The aim of this paper is to implement direct teaching of industrial manipulators using current sensors. The traditional way to implement teaching is either to use a teaching pedant, which is time consuming, or use force sensors, which increases system cost. To overcome these disadvantages, a novel method is explored in the paper by using current sensors installed at joints as torque observers.

The method uses current sensors installed at each joint of a manipulator as torque observers and estimates external forces from differences between joint-driven torque computed based on the values of current sensors and commanded values of motor-driven torque. The joint-driven torque is computed by cancelling out both pre-calibrated gravity and friction resistance (compensation). Also, to make the method robust, the paper presents a strategy to detect unexpected slowly drifts and zero external forces and stop the robot in those situations.

Experimental results demonstrated that compensating the joint torques using both pre-calibrated gravity and friction resistance has performance comparable to a force sensor installed on the end effector of a manipulator. It is possible to implement satisfying direct teaching without using force sensors on 7 degree of freedom manipulators with large mass and friction resistance.

The main contribution of the paper is that the authors cancel out both pre-calibrated gravity and friction resistance to improve the direct teaching using only current sensors; they develop methods to avoid unsafe situations like slow drifts. The method will benefit industrial manipulators, especially those with large mass and friction resistance, to realize flexible and reliable direct teaching.

This paper aims to improve the accuracy of robot payload identification and decrease the complexity in its industrial application by developing a new method based on the actuator current.

Instead of previous general robot dynamic modeling of the actuators, links, together with payload inertial parameters, the paper discovers that the difference of the actuator torque between the robot moving along the same trajectory with and without carrying payload can be described as a function of the payload inertial parameters directly. Then a direct dynamic identification model of payload is built, a set of specialized novel exciting trajectories are designed for accurate identification and the least square method is applied for the estimation of the load parameters.

The experiments confirm the effectiveness of the proposed method in robot payload identification. The identification accuracy is greatly improved compared with that of existing methods based on the actuator current and is close to the accuracy of the methods that direct use the wrist-mounted force-torque sensor.

As the provided experiments indicate, the proposed method expands the application range and greatly improves the accuracy, hence making payload identification fully operational in the industrial application.

The novelty of such an identification method is that it does not require the rotor inertias and inertial parameters of links as a prior knowledge, and the specially designed trajectories provide completed decoupling of the load parameters.

The paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks.

Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method.

The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios.

With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

To promote the intuitiveness of collaborative tasks, the negotiation ability of humans with each other has inspired a large amount of studies aimed at reproducing the capacity in physical human-robot interaction (pHRI). This paper aims to promote mutual adaptation in negotiation when both parties possess incomplete information.

This paper introduces virtual fixtures into the traditional negotiation mechanism, locally regulating tracking trajectory and impedance parameters in the negotiating phase until the final plan integrates bilateral intentions well. In the strategy, robots convey its task information to humans and offer groups of guide plans for them to choose, on the premise of maximizing the robot’s own profits.

Compared with traditional negotiation strategies, humans adapt to robots easily and show lower cognitive load in the method, while the satisfied plan shows better performance for the whole human-robot system.

In this study, this paper proposes a novel negotiation strategy to facilitate the mutual adaptation of humans and robots in complicated shared tasks, especially when both parties possess incomplete information of tasks.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector.

A total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications.

This study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed.

The identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

This paper aims to present a method to improve the sensitive and low probabilities of false alarm of a manipulator in a human–robot interaction environment, which can improve the performance of the system owing to non-linear uncertainty in the model of the robot controller.

A novel collision detection method based on adaptive residual estimation is proposed, promoting the detection accuracy of the collision of the manipulator during operation. First, a general momentum residual estimator is designed to incorporate the non-linear factors of the manipulator (e.g. joint friction, speed and acceleration) into the residual-related uncertainty of the model. Second, model parameters are estimated through gradient correction. The residual filter is used to determine the dynamic threshold, resulting in higher detection accuracy. Finally, the performance of the residual estimation scheme is evaluated by comparing the dynamic threshold with residual in real-time experiments where a single Universal Robot 5 robot end–effector collides with the obstacle.

Experimental results demonstrate that the collision detection system can improve sensitivity and lead to low probabilities of false alarm of non-linear uncertainty in the model.

The method proposed in this article can be applied to industry and human–robot interaction area.

An adaptive collision detection method is proposed in this paper to address non-linear uncertainties of the model in industrial application.